A replenishment strategy prediction method, system, device and storage medium

Abstract

The embodiment of the present application discloses a prediction method, system, device and storage medium for a replenishment strategy, including: obtaining the athlete's monitoring data, current position, historical replenishment status, replenishment station identification and current environmental parameters; calculating the first replenishment distance according to the historical replenishment status and the replenishment station identification, determining the road condition of the target section according to the current position and the event map, and determining the target prediction model according to the road condition; inputting the first replenishment distance, monitoring data and current environmental parameters into the prediction model to generate replenishment prompt information; determining the second replenishment distance from the current position to the next nearest replenishment station based on the event map, and calculating the estimated replenishment time according to the average pace and the second replenishment distance; binding the replenishment prompt information, the estimated replenishment time and the competition number to generate a predicted replenishment strategy. It is possible to predict the corresponding replenishment strategy based on the athlete's actual situation, so that the athlete can accurately judge the best replenishment type and replenishment amount at each supply station.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer prediction technology, and in particular, to a prediction method, system, device and storage medium for a replenishment strategy. Background Art

[0002] During a marathon, athletes' bodies face tremendous stress. Therefore, generating a reasonable replenishment strategy becomes an important guarantee for maintaining athletes' physical fitness and competitive state. Replenishment strategy prediction refers to formulating and adjusting replenishment plans in advance according to factors such as the athletes' physical condition and competition environment during a marathon to ensure that athletes can obtain sufficient energy and water during the competition, maintain their best condition and complete the competition smoothly.

[0003] In the related art, the replenishment strategy is usually predicted based on the historical competition data of the participating athletes, such as predicting that 100-200 ml of water is replenished every 5 kilometers, and energy gel is replenished every 30-40 minutes, etc. However, since the allocation of supply station locations and resource allocation is determined based on the analysis of historical competition data, such as setting up relatively dense supply stations in places with high traffic flow, the distances between the supply stations are not evenly set. Athletes can only choose the current supply items they need at the supply station according to their needs, but cannot accurately grasp the supply station information of the entire process, such as the distance between the current position and the nearest supply station, and the distance between the nearest supply station and the next supply station, etc., so it is impossible to accurately judge the best type and amount of supply, and it is also inconvenient for the supply station personnel to allocate supply resources for the corresponding athletes. Summary of the invention

[0004] The embodiments of the present application provide a method, system, device and storage medium for predicting a supply strategy, which solves the problem that athletes cannot accurately grasp the information of supply stations throughout the entire process and cannot accurately determine the optimal type and amount of supply. The system can predict the corresponding supply strategy based on the athlete's real-time detection data, environmental parameters, road conditions and historical supply information, so that the athlete can accurately determine the optimal type and amount of supply at each supply station, and it is also convenient for supply station personnel to configure supply resources for the corresponding athlete according to the predicted supply strategy.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting a replenishment strategy, comprising:

[0006] Acquire the athlete's monitoring data, current location, historical supply information, and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification;

[0007] Calculating a first refueling distance according to the historical refueling status, the refueling station identifier and the current position, determining a target race section and a road condition of the target race section according to the current position and a preset race map, and determining an associated target prediction model according to the type of the road condition;

[0008] Inputting the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information;

[0009] Determine a second refueling distance from the current position to the next nearest refueling station based on the preset race map, and calculate an estimated refueling time based on the average pace in the monitoring data and the second refueling distance;

[0010] The supply reminder information, the estimated supply time and the athlete's competition number are bound to generate a predicted supply strategy.

[0011] Optionally, the calculating the first replenishment distance according to the historical replenishment status, the replenishment station identifier and the current position includes:

[0012] Determine the refueling status corresponding to the last nearest refueling station identifier according to the historical refueling status and the current position, and if the refueling status is that refueling has been completed, determine the distance of the target stage as the first refueling distance;

[0013] In the case where the replenishment status is unreplenished, determining whether there are multiple target replenishment station identifiers in the unreplenished state that are continuous with the last nearest replenishment station identifier;

[0014] In the case where the plurality of target refueling station identifiers exist, calculating a first refueling distance according to the plurality of target refueling station identifiers;

[0015] In the case where the multiple target supply station identifiers do not exist, the first supply distance is calculated according to the identifier of the previous nearest supply station and the identifiers of two adjacent supply stations.

[0016] Optionally, the multiple target supply station identifiers include a first target supply station identifier and a second target supply station identifier, the first target supply station identifier being the first supply station identifier in an unreplenished state among the multiple target supply stations, and the second target supply station identifier being the last supply station identifier in an unreplenished state among the multiple target supply stations.

[0017] Optionally, calculating the first replenishment distance according to the multiple target replenishment station identifiers includes:

[0018] According to the multiple target supply station identifiers, respectively calculate a first sub-supply distance from the first target supply station to the previous adjacent supply station and a second sub-supply distance from the first target supply station to the second target supply station, the first target supply station being the supply station corresponding to the first target supply station, and the second target supply station being the supply station corresponding to the second target supply station;

[0019] The first sub-supply distance, the second sub-supply distance and the distance of the target stage are summed to obtain a first supply distance.

[0020] Optionally, determining a target race section and a road condition of the target race section according to the current position and a preset race map includes:

[0021] Determine the location of each supply station according to the preset race map, determine the section between two adjacent supply stations as a race section, and determine the race section to which the current position belongs as the target race section;

[0022] The road condition of the target race section is determined according to the preset road detection data and the race section identifier of the target race section.

[0023] Optionally, after generating the forecast replenishment strategy, the method further includes:

[0024] The predicted supply strategy is sent to the athlete and the next nearest supply station respectively, so that the next nearest supply station can monitor the athlete's supply status and update the athlete's historical supply information.

[0025] Optionally, the monitoring data includes race distance, average pace, heart rate data, blood pressure data and body temperature data; the environmental parameters include temperature data, humidity data, wind speed data and air pressure data; and the types of road conditions include flat roads, uphill roads and downhill roads.

[0026] In a second aspect, an embodiment of the present application provides a replenishment strategy prediction system, including:

[0027] A data acquisition module, used to acquire the athlete's monitoring data, current position, historical supply information and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification;

[0028] A first replenishment distance calculation module, used to calculate a first replenishment distance according to the historical replenishment status, the replenishment station identifier and the current position;

[0029] A target prediction model determination module, used to determine a target race section and a road condition of the target race section according to the current position and a preset race map, and to determine an associated target prediction model according to the type of the road condition;

[0030] A prediction module, used for inputting the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information;

[0031] An estimated replenishment time calculation module, used to determine a second replenishment distance from the current position to the next nearest replenishment station based on the preset race map, and calculate an estimated replenishment time according to the average pace in the monitoring data and the second replenishment distance;

[0032] The predicted supply strategy generation module is used to bind the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy.

[0033] In a third aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the replenishment strategy described in the first aspect.

[0034] In a fourth aspect, an embodiment of the present application provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the method for predicting a replenishment strategy as described in the first aspect.

[0035] The embodiment of the present application obtains the athlete's monitoring data, current position, historical supply status, supply station identification and current environmental parameters; calculates a first supply distance based on the historical supply status and the supply station identification, determines a target stage and the road condition of the target stage based on the current position and a preset race map, and determines an associated target prediction model based on the type of road condition; inputs the first supply distance, monitoring data and current environmental parameters into the target prediction model to generate supply prompt information; determines a second supply distance from the current position to the next nearest supply station based on the preset race map, and calculates an estimated supply time based on the average pace in the monitoring data and the second supply distance; binds the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy. In the above scheme, the corresponding supply strategy can be predicted based on the athlete's real-time detection data, environmental parameters, road conditions, and historical supply information, so that the athlete can accurately judge the best supply type and supply amount at each supply station. The athlete's supply time can be predicted by calculating the second supply distance and the athlete's average pace, and a predicted supply strategy containing supply time, athlete number and supply prompt information is generated, which is convenient for supply station personnel to allocate supply resources for the corresponding athletes according to the predicted supply strategy, thereby improving the comprehensiveness and accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a method for predicting a replenishment strategy provided in an embodiment of the present application;

[0037] Figure 2 is a flow chart of a method for calculating a first replenishment distance provided in an embodiment of the present application;

[0038] Figure 3 This is a schematic diagram of the distribution of supply station status provided in an embodiment of the present application;

[0039] Figure 4 is a flow chart of another method for calculating the first replenishment distance provided in an embodiment of the present application;

[0040] Figure 5 It is a prediction system for a replenishment strategy provided in an embodiment of the present application;

[0041] Figure 6 It is a structural schematic diagram of a replenishment strategy prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only part of the present application is shown in the accompanying drawings, but not all of the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0044] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0045] The following, in conjunction with the accompanying drawings, describes in detail the prediction method, system, device and medium of the replenishment strategy provided by the embodiments of the present application through specific embodiments and their application scenarios.

[0046] The method for predicting the replenishment strategy provided in the embodiment of the present application is applied to the scenario where marathon athletes analyze the real-time status of each athlete during the competition, so as to predict the optimal replenishment strategy for each athlete, thereby ensuring the athlete's optimal competition state. Based on the above application scenario, it can be understood that the executor of the embodiment of the present application can be a server.

[0047] Figure 1 is a flow chart of a method for predicting a replenishment strategy provided in an embodiment of the present application, such as Figure 1 As shown, including:

[0048] Step S101, obtaining the athlete's monitoring data, current position, historical supply information and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification.

[0049] Among them, the athlete's monitoring data is used to indicate the athlete's physical condition and competition status during the competition, such as the athlete's breathing rate, sweat volume, and competition time, etc. It can also include competition distance, average pace, heart rate data, blood pressure data, and body temperature data. The competition distance is used to indicate the distance the athlete has currently run, such as running 3 kilometers, and the average pace can be used to indicate the average speed per kilometer. Heart rate data is used to indicate the number of heart beats per minute, which is one of the important indicators for measuring the health status of the human body. Blood pressure data is one of the important indicators for evaluating the health status of the human cardiovascular system. It refers to the lateral pressure exerted on the blood vessel wall when blood flows in the blood vessels. It not only reflects the strength of the heart's pumping function, but is also directly related to the health status of the blood vessels and the blood perfusion of various organs throughout the body. The current location can be the location information obtained by positioning the sports bracelet configured by the marathon athlete through GPS, which can be used to indicate the athlete's current location, such as for determining whether the athlete is currently within the specified track. The historical supply information is used to represent the supply situation of the athlete before the current moment in this competition, such as the historical supply status and the supply station mark, wherein the historical supply status is used to represent whether the athlete supplies in the supply station passed through in history, and can include the supply state and the unsupplied state, and the supply station mark is used to represent the supply station in different positions, and the supply station mark can be a digital mark or a letter mark. The historical supply information can also be used to represent the supply situation of the athlete during the historical marathon competition, such as the historical supply rule. The current environmental parameters are used to represent the environmental conditions during the current competition, such as weather, temperature, etc., and can also include temperature data, humidity data, wind speed data and air pressure data. In one embodiment, the monitoring data, current position, historical supply information of each marathon athlete and the environmental parameters at the athlete's position are periodically obtained, wherein the data acquisition cycle can be a cycle with a shorter time interval, such as 15 minutes.

[0050] Step S102, calculating a first refueling distance according to the historical refueling status, the refueling station identifier and the current position, determining a target race section and a road condition of the target race section according to the current position and a preset race map, and determining an associated target prediction model according to the type of the road condition.

[0051] Among them, the first supply distance can be used to indicate the distance between the supply station at the last supply of the athlete and the next supply station closest to the current position, such as the last supply of the athlete was at the supply station C, and the athlete's current position is between the supply station E and the supply station F, where the supply station F is the nearest next supply station, so the first supply distance is the distance from the supply station C to the supply station F. The event map is a map used to display the race route, race mileage, and geographic information along the way and the location of functional sites in the current marathon. The target stage is used to indicate a certain section of the marathon race. Each stage is pre-divided based on the stage division rules, such as one stage every 5 kilometers or one stage every 1 kilometer. The road condition is used to indicate the real-time situation of the competition road surface, such as the presence of multiple continuous bends or ramps in the competition road surface, so the types of road conditions can include flat roads, uphill roads, and downhill roads. The uphill road surface will increase the athlete's energy consumption accordingly, requiring the athlete to appropriately increase the amount of supply, and the downhill road surface will reduce the athlete's energy consumption accordingly. The athlete can appropriately reduce the amount of supply in the downhill road section. The target prediction model is a model that predicts the amount and type of replenishment based on the athlete's energy consumption and real-time physical condition. Since different road conditions will cause athletes to consume different amounts of energy, different types of road conditions correspond to different prediction models, such as flat road conditions corresponding to prediction model A, uphill road conditions corresponding to prediction model B, and downhill road conditions corresponding to prediction model C.

[0052] In one embodiment, the supply station where the athlete was last replenished is determined based on the historical replenishment status, and the supply station identifier of the supply station where the athlete was last replenished is determined, the supply station identifier of the next nearest supply station is determined based on the current position, and the first replenishment distance between the two supply stations is determined based on the supply station identifier of the supply station where the athlete was last replenished and the supply station identifier of the next nearest supply station. The current position of the athlete is checked in the preset event map, and the target stage to which the current position belongs and the road conditions of the target stage are checked in the event map, and the associated target prediction model is determined based on the type of road conditions.

[0053] In one embodiment, determining the target race section and the road condition of the target race section according to the current position and a preset race map includes:

[0054] Determine the location of each supply station according to the preset race map, determine the section between two adjacent supply stations as a race section, and determine the race section to which the current position belongs as the target race section;

[0055] The road condition of the target race section is determined according to the preset road detection data and the race section identifier of the target race section.

[0056] Among them, the preset road detection data is the road condition actually detected when planning the marathon route, which may include the slope of the road, the scenery on both sides of the road, and whether there are dangerous factors in the road. The stage identification is a unique number used to distinguish each stage. In one embodiment, the relative position relationship of each supply station is determined according to the supply station identification in the preset event map, and the competition section between two adjacent supply stations is determined as a stage, such as determining the competition section between the starting point and the A supply station as the No. 1 stage, and the competition section between the A supply station and the B supply station as the No. 2 stage, and so on, the competition section between the X supply station and the end point is determined as the No. N stage. The current position of the athlete in the event map is determined according to the athlete's positioning information. If the current position is between the A supply station and the B supply station, the target stage is determined to be the No. 2 stage, and the road condition of the No. 2 stage is determined to be a flat road surface, an uphill road surface or a downhill road surface according to the preset road detection data.

[0057] In the embodiment of the present application, the location of each supply station is determined according to the preset competition map, the section between two adjacent supply stations is determined as a stage, and the stage to which the current position belongs is determined as the target stage; the road surface condition of the target stage is determined according to the preset road detection data and the stage mark of the target stage. In the above scheme, the stages are divided according to the competition sections between the supply stations, and the road surface condition of the target stage is used as the prediction condition, so that the judgment area of ​​the road surface condition coincides with the area between the supply stations, which improves the accuracy of the prediction and makes the prediction result more consistent with the actual competition status of the athletes.

[0058] Step S103: input the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information.

[0059] The supply prompt information is used to indicate the supply prompt information required for the current physical condition of the athlete, and may include the type of supply and the amount of supply, etc. In one embodiment, after determining the target prediction model, the first supply distance, the athlete's competition test data, the physical test data, and the current environmental parameters at the athlete's location, etc. are input into the target prediction model to obtain the supply items and the amount of supply, etc. required by the athlete.

[0060] Step S104: determining a second refueling distance from the current position to the next nearest refueling station based on the preset race map, and calculating an estimated refueling time based on the average pace in the monitoring data and the second refueling distance.

[0061] The second supply distance refers to the distance between the athlete's current position and the nearest supply station to be reached. The supply time is the time when the athlete can be replenished for the last time, that is, the time to reach the next nearest supply station. After determining the second supply distance, the athlete's average pace is determined based on the athlete's competition monitoring data, and the time to reach the next nearest supply station is calculated based on the average pace and the second supply distance.

[0062] Step S105: Bind the supply reminder information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy.

[0063] The predicted supply strategy is a plan for instructing athletes to replenish energy and a plan for instructing supply station personnel to supplement and configure supply resources. In one embodiment, the supply prompt information generated by the target prediction model, the calculated estimated supply time and the athlete's competition number are bound to generate the athlete's predicted supply strategy.

[0064] In a possible embodiment, after generating the forecast replenishment strategy, the method further includes:

[0065] The predicted supply strategy is sent to the athlete and the next nearest supply station respectively, so that the next nearest supply station can monitor the athlete's supply status and update the athlete's historical supply information.

[0066] In the above scheme, by sending the predicted supply strategy to the athlete and the next nearest supply station respectively, the athlete can grasp the time of arrival at the next nearest supply station and the best supply type and amount at the next supply station in real time. It can also be convenient for the personnel of the next nearest supply station to configure supply resources in advance according to the athlete's supply needs. When there is a shortage of resources, the supply resources can be replenished in time to avoid affecting the athlete's competition state.

[0067] The embodiment of the present application obtains the athlete's monitoring data, current position, historical supply status, supply station identification and current environmental parameters; calculates a first supply distance based on the historical supply status and the supply station identification, determines a target stage and the road condition of the target stage based on the current position and a preset race map, and determines an associated target prediction model based on the type of road condition; inputs the first supply distance, monitoring data and current environmental parameters into the target prediction model to generate supply prompt information; determines a second supply distance from the current position to the next nearest supply station based on the preset race map, and calculates an estimated supply time based on the average pace in the monitoring data and the second supply distance; binds the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy. In the above scheme, the corresponding supply strategy can be predicted based on the athlete's real-time detection data, environmental parameters, road conditions, and historical supply information, so that the athlete can accurately judge the best supply type and supply amount at each supply station. The athlete's supply time can be predicted by calculating the second supply distance and the athlete's average pace, and a predicted supply strategy containing supply time, athlete number and supply prompt information is generated, which is convenient for supply station personnel to allocate supply resources for the corresponding athletes according to the predicted supply strategy, thereby improving the comprehensiveness and accuracy of the prediction.

[0068] Figure 2 is a flow chart of a method for calculating a first replenishment distance provided in an embodiment of the present application, such as Figure 2 As shown, including:

[0069] Step S201: determining the refueling status corresponding to the last nearest refueling station identifier according to the historical refueling status and the current position, and when the refueling status is that refueling has been completed, determining the distance of the target stage as the first refueling distance.

[0070] Among them, the last nearest supply station identifier is the identifier of the last supply station closest to the current position, and the supply status corresponding to the supply station identifier is determined according to the historical supply status. If the supply status is supplied, the distance of the target stage can be determined as the first supply distance, and the distance from the last supply station to the next nearest supply station can also be determined as the first supply distance. It can be understood that since the first supply distance is used for the prediction model to generate supply prompt information, determining the distance of the target stage as the first supply distance means that when making a prediction, only the impact of the distance of the target stage on the prediction result is considered, which can improve both the accuracy and efficiency of the prediction.

[0071] Step S202: When the replenishment status is not replenished, determine whether there are multiple target replenishment station identifiers in the not replenished status that are consecutive to the last nearest replenishment station identifier.

[0072] The target supply station identifier is a supply station identifier corresponding to the unreplenished state. In one embodiment, the multiple target supply station identifiers include a first target supply station identifier and a second target supply station identifier, the first target supply station identifier being the first supply station identifier in the multiple target supply stations in the unreplenished state, and the second target supply station identifier being the last supply station identifier in the multiple target supply stations in the unreplenished state.

[0073] In one embodiment, if the refueling status corresponding to the last nearest refueling station identifier is not refueled, it is further determined whether the refueling status corresponding to the last refueling station identifier adjacent to the last nearest refueling station identifier is also not refueled. If it is also not refueled, it is considered that there are multiple consecutive target refueling station identifiers in the refueling status. Figure 3 is a schematic diagram of the distribution of supply station status provided in an embodiment of the present application, such as Figure 3 As shown, the supply status corresponding to supply station A is supplied, and the supply status corresponding to supply stations B, C and D are all unsupplied. It can be determined that B, C and D are all target supply station identifiers. Since the athlete's current position is between supply stations D and E, the last nearest supply station identifier is D, and supply stations C and D are adjacent supply stations, and supply stations B and C are adjacent supply stations. Therefore, it can be determined that B and C are target supply station identifiers in an unsupplied state that are continuous with the last nearest supply station identifier D, wherein B is the first target supply station and D is the second target supply station.

[0074] Step S203: If there are multiple target refueling station identifiers, calculate a first refueling distance according to the multiple target refueling station identifiers.

[0075] Exemplarily, when there are multiple target supply station identifiers, the supply station identifier B and the supply station identifier D can be respectively input into the preset distance measurement module to calculate the first supply distance between the B supply station and the D supply station.

[0076] Step S204: If the multiple target supply station identifiers do not exist, calculate a first supply distance according to the identifier of the previous nearest supply station and the identifiers of two adjacent supply stations.

[0077] In one embodiment, when there are no multiple target supply station identifiers, two supply station identifiers adjacent to the last nearest supply station identifier are determined, and the distances between the last nearest supply station and the two adjacent supply stations are calculated based on the last nearest supply station identifier and the two adjacent supply station identifiers, and the sum of the distances between the last nearest supply station and the two adjacent supply stations is determined as the first supply distance. Figure 3It can be seen that the last nearest supply station is marked as D, and the two supply stations adjacent to mark D are marked as C and E. The supply station mark C and the supply station mark E are respectively input into the preset distance measurement module, and the first supply distance between the supply station C and the supply station E is calculated, that is, the sum of the distances from the last nearest supply station D to the two adjacent supply stations C and E is determined as the first supply distance.

[0078] In the embodiment of the present application, the supply status corresponding to the last nearest supply station mark is determined according to the historical supply status. When the supply status is supplied, the distance of the target stage is determined as the first supply distance; when the supply status is not supplied, it is determined whether there are multiple target supply station marks that are in the unsupplied state and are continuous with the last nearest supply station mark; when there are multiple target supply station marks, the first supply distance is calculated according to the continuous target supply station marks; when there are no multiple target supply station marks, the first supply distance is calculated according to the distance between the last nearest supply station mark and the two adjacent supply stations. In the above scheme, the calculation method of the corresponding first supply distance can be determined in real time according to the athlete's historical supply status, thereby ensuring the rationality of the first supply distance and improving the prediction efficiency and accuracy of the subsequent target prediction model.

[0079] Figure 4 is a flow chart of another method for calculating the first replenishment distance provided in an embodiment of the present application, such as Figure 4 As shown, including:

[0080] Step S301: Calculate a first sub-supply distance from a first target supply station to a previous adjacent supply station and a second sub-supply distance from the first target supply station to a second target supply station according to the multiple target supply station identifiers, wherein the first target supply station is the supply station corresponding to the first target supply station, and the second target supply station is the supply station corresponding to the second target supply station.

[0081] Step S302: summing up the first sub-supply distance, the second sub-supply distance and the distance of the target race segment to obtain a first supply distance.

[0082] The first sub-replenishment distance is the distance between the first target refilling station and the last target refilling station among the multiple consecutive target refilling stations. The second sub-replenishment distance is the distance between the first target refilling station and the last target refilling station among the multiple consecutive target refilling stations.

[0083] In one embodiment, since the first target supply station is identified as the first supply station in an unreplenished state among multiple target supply stations, the supply state corresponding to the last adjacent supply station of the first target supply station is a replenished state, that is, the supply station that has been replenished most recently. The distance from the supply station to the first target supply station is calculated based on the identification of the supply station and the identification of the first target supply station. The distance from the first target supply station to the second target supply station is calculated based on the identification of the first target supply station and the identification of the second target supply station. For example, Figure 3 As shown, the distance between supply station B and supply station A, that is, the first sub-supply distance, and the distance between supply station B and supply station D, that is, the second sub-supply distance, are calculated respectively, and the first sub-supply distance and the second sub-supply distance are summed to obtain the first supply distance.

[0084] In another possible embodiment, the distance between the second target refueling station corresponding to the second target refueling station identifier and the next nearest refueling station can also be calculated as the third sub-refueling distance, that is, Figure 3 The distance between the D supply station and the E supply station shown is the third sub-supply distance. Accordingly, the first sub-supply distance, the second sub-supply distance and the third sub-supply distance are summed to obtain the first supply distance.

[0085] Figure 5 is a prediction system for a replenishment strategy provided by an embodiment of the present application, such as Figure 5 As shown, including:

[0086] A data acquisition module 41 is used to acquire the athlete's monitoring data, current position, historical supply information and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification;

[0087] A first replenishment distance calculation module 42, configured to calculate a first replenishment distance according to the historical replenishment status and the replenishment station identifier;

[0088] A target prediction model determination module 43 is used to determine a target race section and a road condition of the target race section according to the current position and a preset race map, and to determine an associated target prediction model according to the type of the road condition;

[0089] A prediction module 44, configured to input the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information;

[0090] An estimated refueling time calculation module 45 is used to determine a second refueling distance from the current position to the next nearest refueling station based on the preset race map, and calculate an estimated refueling time according to the average pace in the monitoring data and the second refueling distance;

[0091] The predicted supply strategy generating module 46 is used to bind the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy.

[0092] The embodiment of the present application obtains the athlete's monitoring data, current position, historical supply status, supply station identification and current environmental parameters; calculates a first supply distance based on the historical supply status and the supply station identification, determines a target stage and the road condition of the target stage based on the current position and a preset race map, and determines an associated target prediction model based on the type of road condition; inputs the first supply distance, monitoring data and current environmental parameters into the target prediction model to generate supply prompt information; determines a second supply distance from the current position to the next nearest supply station based on the preset race map, and calculates an estimated supply time based on the average pace in the monitoring data and the second supply distance; binds the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy. In the above scheme, the corresponding supply strategy can be predicted based on the athlete's real-time detection data, environmental parameters, road conditions, and historical supply information, so that the athlete can accurately judge the best supply type and supply amount at each supply station. The athlete's supply time can be predicted by calculating the second supply distance and the athlete's average pace, and a predicted supply strategy containing supply time, athlete number and supply prompt information is generated, which is convenient for supply station personnel to allocate supply resources for the corresponding athletes according to the predicted supply strategy, thereby improving the comprehensiveness and accuracy of the prediction.

[0093] In a possible embodiment, the first replenishment distance calculation module 42 is specifically used for:

[0094] Determine the refueling status corresponding to the last nearest refueling station identifier according to the historical refueling status and the current position, and if the refueling status is that refueling has been completed, determine the distance of the target stage as the first refueling distance;

[0095] In the case where the replenishment status is unreplenished, determining whether there are multiple target replenishment station identifiers in the unreplenished state that are continuous with the last nearest replenishment station identifier;

[0096] In the case where the plurality of target refueling station identifiers exist, calculating a first refueling distance according to the plurality of target refueling station identifiers;

[0097] In the case where the multiple target supply station identifiers do not exist, the first supply distance is calculated according to the identifier of the previous nearest supply station and the identifiers of two adjacent supply stations.

[0098] In a possible embodiment, the multiple target supply station identifiers include a first target supply station identifier and a second target supply station identifier, the first target supply station identifier being the first supply station identifier in an unreplenished state among the multiple target supply stations, and the second target supply station identifier being the last supply station identifier in an unreplenished state among the multiple target supply stations.

[0099] In a possible embodiment, the first replenishment distance calculation module 42 is further configured to:

[0100] According to the multiple target supply station identifiers, respectively calculate a first sub-supply distance from the first target supply station to the previous adjacent supply station and a second sub-supply distance from the first target supply station to the second target supply station, the first target supply station being the supply station corresponding to the first target supply station, and the second target supply station being the supply station corresponding to the second target supply station;

[0101] The first sub-supply distance, the second sub-supply distance and the distance of the target stage are summed to obtain a first supply distance.

[0102] In a possible embodiment, the target prediction model determination module 43 is specifically used to:

[0103] Determine the location of each supply station according to the preset race map, determine the section between two adjacent supply stations as a race section, and determine the race section to which the current position belongs as the target race section;

[0104] The road condition of the target race section is determined according to the preset road detection data and the race section identifier of the target race section.

[0105] In a possible embodiment, the policy push module is used to:

[0106] The predicted supply strategy is sent to the athlete and the next nearest supply station respectively, so that the next nearest supply station can monitor the athlete's supply status and update the athlete's historical supply information.

[0107] In a possible embodiment, the monitoring data includes race distance, average pace, heart rate data, blood pressure data and body temperature data; the environmental parameters include temperature data, humidity data, wind speed data and air pressure data; and the types of road conditions include flat roads, uphill roads and downhill roads.

[0108] The embodiment of the present application also provides a replenishment strategy prediction device, which can integrate a replenishment strategy prediction system provided by the embodiment of the present application. Figure 6 is a schematic diagram of a replenishment strategy prediction device provided in an embodiment of the present application, with reference to Figure 6 The replenishment strategy prediction device includes: an input device 53, an output device 54, a memory 52 and one or more processors 51; the memory 52 is used to store one or more programs; when one or more programs are executed by one or more processors 51, the one or more processors 51 implement the replenishment strategy prediction method provided in the above embodiment. The input device 53, the output device 54, the memory 52 and the processor 51 can be connected by a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0109] The memory 52, as a computing device readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the replenishment strategy prediction method provided in any embodiment of the present application. The memory 52 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application required for a function; the data storage area may store data created according to the use of the device, etc. In addition, the memory 52 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 52 may further include a memory remotely arranged relative to the processor 51, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0110] The input device 53 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 54 may include a display device such as a display screen.

[0111] The processor 51 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 52, that is, realizes the above-mentioned prediction method of the replenishment strategy.

[0112] The above-mentioned replenishment strategy prediction system, device and computer can be used to execute the replenishment strategy prediction method provided by any of the above-mentioned embodiments, and have corresponding functions and beneficial effects.

[0113] The present application also provides a storage medium storing computer executable instructions. When the computer executable instructions are executed by a computer processor, the method for predicting a replenishment strategy provided in the above embodiment is used to execute the replenishment strategy prediction method, which includes:

[0114] Acquire the athlete's monitoring data, current location, historical supply information, and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification;

[0115] Calculating a first refueling distance according to the historical refueling status and the refueling station identifier, determining a target race section and a road condition of the target race section according to the current position and a preset race map, and determining an associated target prediction model according to the type of the road condition;

[0116] Inputting the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information;

[0117] Determine a second refueling distance from the current position to the next nearest refueling station based on the preset race map, and calculate an estimated refueling time based on the average pace in the monitoring data and the second refueling distance;

[0118] The supply reminder information, the estimated supply time and the athlete's competition number are bound to generate a predicted supply strategy.

[0119] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, which is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (for example, in different computer systems connected by a network). The storage medium may store program instructions (for example, embodied as a computer program) that can be executed by one or more processors.

[0120] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present application is not limited to the prediction method of the supply strategy as described above, and can also execute related operations in the prediction method of the supply strategy provided in any embodiment of the present application.

[0121] The replenishment strategy prediction system, device and storage medium provided in the above embodiments can execute the replenishment strategy prediction method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the replenishment strategy prediction method provided in any embodiment of the present application.

[0122] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for predicting a replenishment strategy, characterized in that: include: Acquire the athlete's monitoring data, current location, historical supply information, and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification; Calculating a first refueling distance according to the historical refueling status, the refueling station identifier and the current position, determining a target race section and a road condition of the target race section according to the current position and a preset race map, and determining an associated target prediction model according to the type of the road condition; Inputting the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information; Determine a second refueling distance from the current position to the next nearest refueling station based on the preset race map, and calculate an estimated refueling time based on the average pace in the monitoring data and the second refueling distance; The supply reminder information, the estimated supply time and the athlete's competition number are bound to generate a predicted supply strategy.

2. The method for predicting a replenishment strategy according to claim 1, characterized in that: The calculating the first replenishment distance according to the historical replenishment status, the replenishment station identifier and the current position includes: Determine the replenishment status corresponding to the last nearest replenishment station identifier according to the historical replenishment status and the current position, and when the replenishment status is replenished, determine the distance from the last replenishment station to the next nearest replenishment station as the first replenishment distance; In the case where the replenishment status is unreplenished, determining whether there are multiple target replenishment station identifiers in the unreplenished state that are continuous with the last nearest replenishment station identifier; In the case where the plurality of target refueling station identifiers exist, calculating a first refueling distance according to the plurality of target refueling station identifiers; In the case where the multiple target supply station identifiers do not exist, the first supply distance is calculated according to the identifier of the previous nearest supply station and the identifiers of two adjacent supply stations.

3. The method for predicting a replenishment strategy according to claim 2, characterized in that: The multiple target supply station identifiers include a first target supply station identifier and a second target supply station identifier, the first target supply station identifier being the first supply station identifier in the multiple target supply stations that is in an unreplenished state, and the second target supply station identifier being the last supply station identifier in the multiple target supply stations that is in an unreplenished state.

4. The method for predicting replenishment strategy according to claim 3, characterized in that: The calculating the first replenishment distance according to the multiple target replenishment station identifiers includes: According to the multiple target supply station identifiers, respectively calculate a first sub-supply distance from the first target supply station to the previous adjacent supply station and a second sub-supply distance from the first target supply station to the second target supply station, the first target supply station being the supply station corresponding to the first target supply station, and the second target supply station being the supply station corresponding to the second target supply station; The first sub-supply distance, the second sub-supply distance and the distance of the target stage are summed to obtain a first supply distance.

5. The method for predicting replenishment strategy according to claim 1, characterized in that: The determining of the target race section and the road condition of the target race section according to the current position and the preset race map includes: Determine the location of each supply station according to the preset race map, determine the section between two adjacent supply stations as a race section, and determine the race section to which the current position belongs as the target race section; The road condition of the target race section is determined according to the preset road detection data and the race section identifier of the target race section.

6. The method for predicting replenishment strategy according to claim 1, characterized in that: After generating the forecast replenishment strategy, the method further includes: The predicted supply strategy is sent to the athlete and the next nearest supply station respectively, so that the next nearest supply station can monitor the athlete's supply status and update the athlete's historical supply information.

7. The method for predicting a replenishment strategy according to any one of claims 1 to 6, characterized in that: The monitoring data include race distance, average pace, heart rate data, blood pressure data and body temperature data; the environmental parameters include temperature data, humidity data, wind speed data and air pressure data; the types of road conditions include flat road, uphill road and downhill road.

8. A replenishment strategy prediction system, characterized in that: include: A data acquisition module, used to acquire the athlete's monitoring data, current position, historical supply information and current environmental parameters, wherein the historical supply information includes historical supply status and supply station identification; A first replenishment distance calculation module, used to calculate a first replenishment distance according to the historical replenishment status, the replenishment station identifier and the current position; A target prediction model determination module, used to determine a target race section and a road condition of the target race section according to the current position and a preset race map, and to determine an associated target prediction model according to the type of the road condition; A prediction module, used for inputting the first replenishment distance, the monitoring data and the current environmental parameters into the target prediction model to generate replenishment prompt information; An estimated replenishment time calculation module, used to determine a second replenishment distance from the current position to the next nearest replenishment station based on the preset race map, and calculate an estimated replenishment time according to the average pace in the monitoring data and the second replenishment distance; The predicted supply strategy generation module is used to bind the supply prompt information, the estimated supply time and the athlete's competition number to generate a predicted supply strategy.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the replenishment strategy prediction method as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the replenishment strategy prediction method according to any one of claims 1 to 7 when executed by a computer processor.

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