CN113128131A - Step-counting cheating detection method and device - Google Patents
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Abstract
The invention discloses a step-counting cheating detection method and a step-counting cheating detection device, which relate to the technical field of artificial intelligence, and the method comprises the following steps: extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user; inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state; and determining whether the step counting process of the user is cheated according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment. The invention can analyze the user state and the equipment state, determine whether the step counting process of the user is cheated or not, automatically detect the step counting cheating, save manpower and material resources and improve the detection efficiency and accuracy of the step counting cheating detection.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a step-counting cheating detection method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the increased awareness of overall fitness, there is an increasing number of games and competitions involving running, such as "walking on the go" activities. At the present stage, the step counting process of running can be recorded in various ways, such as APP in a smart phone and wearable smart devices (such as smart band).
But due to the ranking, scoring and rewarding mechanisms of the running activities, the situation that participants cheat by using cheating means is easily caused. Therefore, how to perform cheating detection on the step counting process becomes a problem to be solved urgently at present.
At present, when step-counting cheating detection is carried out, the method mainly comprises the following two methods of manual detection and hardware equipment addition:
1. manual detection: the method comprises the steps of APP (application) step counting in the smart phone, wherein a gyroscope is mainly used for judging the moving and walking conditions of a user, and because the APP step counting data are complex, the user can be judged whether to cheat by checking the moving conditions of the user within a certain time only in a manual experience mode at present;
2. hardware equipment is added: a user wears hardware equipment used for cheating detection on the feet, and cheating judgment is carried out on the distance change of the feet in the exercise process by means of the hardware equipment;
although the two methods can carry out cheating detection, the following problems can occur:
1. even if the detection of detection personnel is abundant, the manual detection method inevitably causes the condition of judgment error, leads to inaccurate step counting cheating detection, consumes a large amount of manpower and material resources, causes resource waste and cannot be popularized and used in a large area;
2. the method for adding the hardware equipment not only increases the cost of the running activity initiator, but also can cause the situation that a user participating in the running activity needs to purchase the hardware equipment with extra cost, increases the difficulty of the user participating in the activity, is not beneficial to the popularization of the activity, and meanwhile, the cheating detection hardware equipment is worn on the foot, so that the running activity of the user is inconvenient, and the use experience of the user is reduced.
Disclosure of Invention
The embodiment of the invention provides a step-counting cheating detection method, which relates to the technical field of artificial intelligence and is used for improving the step-counting cheating detection accuracy and detection efficiency, and the method comprises the following steps:
extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user; the device state label is used for describing the state of the user device for recording user step counting data;
inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state;
and determining whether the step counting process of the user is cheated according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
The embodiment of the invention also provides a step-counting cheating detection device, which relates to the technical field of artificial intelligence and is used for improving the accuracy and the detection efficiency of step-counting cheating detection, and the device comprises:
the system comprises a tag extraction module, a step counting module and a step counting module, wherein the tag extraction module is used for extracting a user equipment state tag, a user motion behavior tag and a user biological characteristic information tag from current step counting data of a user; the device state label is used for describing the state of the user device for recording user step counting data;
the user state analysis module is used for inputting the user motion behavior label and the user biological characteristic information label into the step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
the equipment state analysis module is used for comparing and analyzing the user equipment state label with preset equipment state judgment conditions to obtain an analysis result of the user equipment state;
and the cheating result analysis module is used for determining whether the step counting process of the user cheats according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the step-counting cheating detection method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the step-counting cheating detection method.
In the embodiment of the invention, a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from the current step counting data of a user; the device state label is used for describing the state of the user device for recording user step counting data; inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user; comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state; whether the step-counting process of the user is cheated or not is determined according to the analysis result of the step-counting cheating regression model and the analysis result of the state of the user equipment, so that the state of the user and the state of the equipment can be analyzed through the extracted state tag of the user equipment, the state tag of the user movement behavior and the extracted biological characteristic information tag of the user, whether the step-counting process of the user is cheated or not is determined, step-counting cheating detection can be automatically performed, the situations that in the prior art, step-counting cheating detection needs to be performed manually, the detection result is inaccurate and a large amount of manpower and material resources are consumed are avoided, cheating detection does not need to be performed manually and depending on experience, manpower and material resources are saved, step-counting cheating detection is automatically achieved, and detection efficiency and detection accuracy of step-counting cheating detection are improved; meanwhile, the problem that in the prior art, the extra cost is increased due to the fact that a user needs to additionally wear and add hardware equipment for step counting cheating detection is solved, the user and a step counting activity initiator do not need to invest funds for the hardware equipment, manpower and material resources are saved, the problem that the user is inconvenient to move due to wearing of the hardware equipment is solved, the use experience of the user is improved, and the running activity initiator is favorable for popularizing activities.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic flow chart illustrating a step-counting cheating detection method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an embodiment of a step-counting cheating detection method according to the present invention;
FIG. 3 is a diagram illustrating an embodiment of a step-counting cheating detection method according to the present invention;
FIG. 4 is a diagram illustrating an embodiment of a step-counting cheating detection method according to the present invention;
FIG. 5 is a diagram illustrating an embodiment of a step-counting cheating detection method according to the present invention;
FIG. 6 is a schematic structural diagram of a step-counting cheating-detecting device according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating an embodiment of a step-counting cheating-detecting device according to the present invention;
FIG. 8 is a diagram illustrating an embodiment of a step-counting cheating-detecting device according to the present invention;
FIG. 9 is a diagram illustrating an embodiment of a step-counting cheating-detecting device according to the present invention;
FIG. 10 is a block diagram schematically illustrating the system configuration of a computer device for step-counting cheating detection in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Firstly, judging the moving and walking conditions of a user by using a gyroscope for each large step counting APP at present, and judging whether the user cheats by checking the moving conditions of the user within a certain time only in a manual experience mode;
and secondly, wearing the hardware equipment on the feet of the user by adding the hardware equipment, and judging whether the distance change of the feet is cheated in the exercise process.
Although the two methods can carry out cheating detection, the following problems can occur:
firstly, the manual experience method has inaccurate judgment result, wastes manpower, and cannot be popularized and used in a large area.
Secondly, hardware equipment needs to be added, the cost of an activity initiator is increased, the difficulty of participation of a user in the activity is increased, and the popularization of the activity is not facilitated
In order to solve the above problems, an embodiment of the present invention provides a step-counting cheating detection method, which relates to the technical field of artificial intelligence, and can perform cheating judgment by using a software method and a machine learning method, so as to save cheating cost and difficulty in cheating prevention, and standardize activity performance, so as to improve accuracy and detection efficiency of step-counting cheating detection, as shown in fig. 1, the method may include:
step 101: extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user; the device state label is used for describing the state of the user device for recording the user pace counting data;
step 102: inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
step 103: comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state;
step 104: and determining whether the step counting process of the user is cheated according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
In the embodiment of the invention, a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from the current step counting data of a user; the device state label is used for describing the state of the user device for recording the user pace counting data; inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user; comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state; whether the step-counting process of the user is cheated or not is determined according to the analysis result of the step-counting cheating regression model and the analysis result of the state of the user equipment, so that the state of the user and the state of the equipment can be analyzed through the extracted state tag of the user equipment, the state tag of the user movement behavior and the extracted biological characteristic information tag of the user, whether the step-counting process of the user is cheated or not is determined, step-counting cheating detection can be automatically performed, the situations that in the prior art, step-counting cheating detection needs to be performed manually, the detection result is inaccurate and a large amount of manpower and material resources are consumed are avoided, cheating detection does not need to be performed manually and depending on experience, manpower and material resources are saved, step-counting cheating detection is automatically achieved, and detection efficiency and detection accuracy of step-counting cheating detection are improved; meanwhile, the problem that in the prior art, the extra cost is increased due to the fact that a user needs to additionally wear and add hardware equipment for step counting cheating detection is solved, the user and a step counting activity initiator do not need to invest funds for the hardware equipment, manpower and material resources are saved, the problem that the user is inconvenient to move due to wearing of the hardware equipment is solved, the use experience of the user is improved, and the running activity initiator is favorable for popularizing activities.
When the method is specifically implemented, firstly, a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from current step counting data of a user; the device status tag is used to describe the status of the user device that records the user's pace data.
In an embodiment, the user athletic performance tag includes: labels respectively used for representing the motion track, the motion duration and the motion frequency of a user; the user biometric information tag includes: labels for characterizing the heartbeat and body temperature, respectively, of a user while exercising. The status of the user device of the user step counting data may include normal and abnormal.
In the above embodiment, the user equipment status tag may include: simulating a positioning tag and a connecting power supply tag; the simulation positioning tag is used for describing whether the user equipment starts simulation positioning or not; the connection power label is used for describing whether the user equipment is connected with a power supply.
In the embodiment, the user state and the equipment state can be analyzed through the extracted user equipment state label, the user motion behavior label and the user biological characteristic information label, whether the step counting process of the user cheats or not is determined, and step counting cheating detection can be automatically carried out.
In specific implementation, after a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from the current step-counting data of a user, the user motion behavior label and the user biological characteristic information label are input into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model for the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user.
In the embodiment, the user equipment state label, the user movement behavior label and the user biological characteristic information label are extracted from the current step counting data of the user through the step counting cheating regression model and are analyzed, and the analysis result of the step counting cheating regression model on the user state can be obtained.
In the above embodiment, can automize and count step cheating and detect, need rely on the manual work to count step cheating and detect under having avoided prior art, and the testing result that causes is inaccurate and consume the condition of a large amount of manpower and materials and take place, no longer need the manual work to rely on experience to carry out the detection of practising fraud, practiced thrift manpower and materials resources, detect through automizing and realizing counting step cheating, promoted the detection efficiency and the degree of accuracy that count step cheating detected.
When the method is specifically implemented, the user motion behavior label and the user biological characteristic information label are input into the step-counting cheating regression model, after the analysis result of the step-counting cheating regression model on the user state is obtained, the user equipment state label is compared and analyzed with the preset equipment state judgment condition, and the analysis result of the user equipment state is obtained.
In an embodiment, the ue status tag may include an analog positioning tag; the simulation positioning tag is used for describing whether the user equipment starts simulation positioning or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether the preset equipment is started in a simulated positioning mode or not and whether the equipment state is abnormal or not;
comparing and analyzing the user equipment state label with the preset equipment state judgment condition to obtain an analysis result of the user equipment state, as shown in fig. 2, the method may include:
step 201: determining whether the user equipment starts simulated positioning according to the simulated positioning tag;
step 202: when the user equipment is determined to start the analog positioning, if the preset equipment state judgment condition is as follows: and when the equipment simulation positioning is started, determining that the state of the user equipment is abnormal if the corresponding equipment state is an abnormal state.
For example, if the rule set by the activity initiator is: when the simulated positioning of the equipment of the user is determined to be started, determining that the equipment state is abnormal; when the device simulation positioning described by the simulation positioning tag in the user device state tags is started, the state of the user device can be determined to be abnormal.
In specific implementation, the device status tag may include a power connection tag; the power connection label is used for describing whether the user equipment is connected with a power supply or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether a preset equipment connection power supply is started or not and whether the equipment state is abnormal or not;
comparing and analyzing the user equipment state label with the preset equipment state judgment condition to obtain an analysis result of the user equipment state, as shown in fig. 3, the method may include:
step 301: determining whether the user equipment is connected with a power supply or not according to the power supply connection label;
step 302: when the user equipment is determined to be connected with the power supply, if the preset equipment state judgment condition is as follows: and when the equipment is connected with the power supply and the corresponding equipment state is an abnormal state, determining that the user equipment state is abnormal.
For example, if the rule set by the activity initiator is: when the connection power supply of the equipment of the user is determined, determining that the equipment state is abnormal; when the equipment described by the power connection label in the user equipment state labels is connected with the power supply, the state of the user equipment can be determined to be abnormal.
In specific implementation, after the user equipment state label is compared and analyzed with the preset equipment state judgment condition to obtain the analysis result of the user equipment state, whether the step-counting process of the user is cheated or not is determined according to the analysis result of the step-counting cheating regression model and the analysis result of the user equipment state.
In an embodiment, determining whether the step-counting process of the user is cheated according to an analysis result of the step-counting cheating regression model and an analysis result of the state of the user equipment may include:
if the analysis result of the step-counting cheating regression model shows that: the user motion behavior label/user biological characteristic information label in the current step counting data of the user is abnormal, and/or the analysis result of the user equipment state shows that: and if the user equipment state is abnormal, determining that the step counting process of the user is cheated.
In the above embodiment, when any one of the user motion behavior tag and/or the user biometric information tag in the current step-counting data of the user and the user equipment state is abnormal, it may be determined that the user cheats in the step-counting process. Whether the step counting process of the user is cheated or not is determined, step counting cheating detection can be automatically carried out, the condition that step counting cheating detection needs to be carried out manually under the prior art is avoided, the detection result is inaccurate, a large amount of manpower and material resources are consumed is avoided, cheating detection does not need to be carried out manually by depending on experience, manpower and material resources are saved, step counting cheating detection is automatically achieved, and the detection efficiency and the accuracy of the step counting cheating detection are improved.
In specific implementation, the step-counting cheating detection method provided in the embodiment of the present invention, as shown in fig. 4, may further include: a step-counting cheating regression model is established as follows:
step 401: extracting historical records of the user movement behavior label and the user biological characteristic information label from the acquired user historical step counting data;
step 402: and performing regression analysis and modeling on the historical records of the user motion behavior tags and the user biological characteristic information tags by using a machine learning algorithm to obtain a step-counting cheating regression model.
In an embodiment, the regression analysis includes: linear regression analysis, and/or GBDT regression analysis.
In the above embodiment, the step-counting cheating regression model is used to perform regression analysis on the historical records of the user motion behavior tag and the user biological characteristic information tag, and the user motion behavior tag and the user biological characteristic information tag extracted from the current step-counting data of the user, so as to determine whether the user motion behavior tag and/or the user biological characteristic information tag are/is abnormal in the current step-counting data of the user.
In specific implementation, the step-counting cheating detection method provided in the embodiment of the present invention may further include:
and when determining that the step counting process of the user is cheated, sending warning information of the cheating in the step counting process of the user.
In the embodiment, the running activity monitoring method and the running activity monitoring system help an activity initiator to monitor the running activity by sending the cheating warning information in the step counting process of the user to the manager.
In specific implementation, the step-counting cheating detection method provided in the embodiment of the present invention may further include:
and recording the analysis result of the step counting cheating regression model, the analysis result of the state of the user equipment and the result of determining whether the step counting process of the user is cheated or not to generate a record file.
In the embodiment, the record file is generated, so that the staff can trace the running data of the activity participants, the running activity can be inquired when the activity participants initiate objections, and the final determination of whether the activity participants cheat or not can be facilitated.
In specific implementation, the step-counting cheating detection method provided in the embodiment of the present invention, as shown in fig. 5, may further include:
step 501: continuously extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from step counting data of a user;
step 502: and carrying out iterative optimization on the step-counting cheating regression model by using the continuously extracted user equipment state label, the user motion behavior label and the user biological characteristic information label.
In the embodiment, the step counting data of the user is continuously acquired, and the user equipment state label, the user motion behavior label and the user biological characteristic information label are continuously acquired, so that the accuracy of the step counting cheating regression model is favorably improved, the user state and the equipment state are favorably analyzed, whether the step counting process of the user cheats or not is determined, and the accuracy of automatic step counting cheating detection is improved.
A specific embodiment is given below to illustrate a specific application of the method of the present invention, and in this embodiment, the following steps may be included:
step 1: collecting historical step counting data of a user;
step 2: inputting business rules, namely inputting the business rules formulated by business personnel; the business rules may include: the corresponding relation between whether the equipment is started or not by the simulation positioning of the specified equipment and whether the equipment state is abnormal or not is initiated by the activity. And/or whether the equipment is connected with the power supply and is started or not and whether the equipment state is abnormal or not;
and step 3: extracting historical records of a user equipment state label, a user motion behavior label and a user biological characteristic information label from the historical step counting data of the user in the step 1;
and 4, step 4: the historical records of the user equipment state label, the user movement behavior label and the user biological characteristic information label extracted in the step 2 are transmitted to a step-counting cheating regression model;
and 5: extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user, inputting the user equipment state label, the user motion behavior label and the user biological characteristic information label into the step counting cheating regression model and the service rule obtained in the step 4, and predicting;
step 6: fusing the two results obtained in the step 5;
and 7: and visually displaying the result in the step 6.
Of course, it is understood that other variations of the above detailed flow can be made, and all such variations are intended to fall within the scope of the present invention.
In the embodiment of the invention, a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from the current step counting data of a user; the device state label is used for describing the state of the user device for recording the user pace counting data; inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user; comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state; whether the step-counting process of the user is cheated or not is determined according to the analysis result of the step-counting cheating regression model and the analysis result of the state of the user equipment, so that the state of the user and the state of the equipment can be analyzed through the extracted state tag of the user equipment, the state tag of the user movement behavior and the extracted biological characteristic information tag of the user, whether the step-counting process of the user is cheated or not is determined, step-counting cheating detection can be automatically performed, the situations that in the prior art, step-counting cheating detection needs to be performed manually, the detection result is inaccurate and a large amount of manpower and material resources are consumed are avoided, cheating detection does not need to be performed manually and depending on experience, manpower and material resources are saved, step-counting cheating detection is automatically achieved, and detection efficiency and detection accuracy of step-counting cheating detection are improved; meanwhile, the problem that in the prior art, the extra cost is increased due to the fact that a user needs to additionally wear and add hardware equipment for step counting cheating detection is solved, the user and a step counting activity initiator do not need to invest funds for the hardware equipment, manpower and material resources are saved, the problem that the user is inconvenient to move due to wearing of the hardware equipment is solved, the use experience of the user is improved, and the running activity initiator is favorable for popularizing activities.
As described above, in the embodiment of the invention, the key label influencing whether cheating exists is extracted through the historical data of the 'walking by vigorous strides' activity user, the step-counting cheating model is constructed through a machine learning method, and meanwhile, the judgment of the specified business rule of the activity initiator is supported. The embodiment of the invention can extract the tag data by using the historical data of the user, further construct the step-counting cheating model by using the tag data, enable the business personnel of the activity initiator to input the business rules to intervene the cheating result, utilize the step-counting cheating model to act on the final result after being fused with the business result judgment, and utilize a software method to judge the cheating by a machine learning method, thereby saving the cheating cost and the difficulty of cheating prevention, standardizing the activity, being more beneficial to the development of the activity and protecting the benefits of the activity initiator.
The embodiment of the invention also provides a step-counting cheating detection device, which is as described in the following embodiment. Because the principle of solving the problems of the device is similar to that of the step-counting cheating detection method, the implementation of the device can refer to the implementation of the step-counting cheating detection method, and repeated parts are not described again.
An embodiment of the present invention further provides a step-counting cheating detection apparatus, which relates to the technical field of artificial intelligence and is used for improving the accuracy and detection efficiency of step-counting cheating detection, as shown in fig. 6, the apparatus includes:
the tag extraction module 01 is used for extracting a user equipment state tag, a user motion behavior tag and a user biological characteristic information tag from the current step counting data of the user; the device state label is used for describing the state of the user device for recording the user pace counting data;
the user state analysis module 02 is used for inputting the user movement behavior tag and the user biological characteristic information tag into the step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
the device state analyzing module 03 is configured to compare and analyze the user device state label with a preset device state judgment condition to obtain an analysis result of the user device state;
and the cheating result analysis module 04 is used for determining whether the step-counting process of the user cheats according to the analysis result of the step-counting cheating regression model and the analysis result of the state of the user equipment.
In one embodiment, the user athletic performance tag includes: labels respectively used for representing the motion track, the motion duration and the motion frequency of a user; the user biometric information tag includes: labels for characterizing the heartbeat and body temperature, respectively, of a user while exercising.
In an embodiment, the step-counting cheating detection apparatus provided in an embodiment of the present invention, as shown in fig. 7, may further include:
establishing a step-counting cheating regression model module 05 for:
a step-counting cheating regression model is established as follows:
extracting historical records of the user movement behavior label and the user biological characteristic information label from the acquired user historical step counting data;
and performing regression analysis and modeling on the historical records of the user motion behavior tags and the user biological characteristic information tags by using a machine learning algorithm to obtain a step-counting cheating regression model.
In one embodiment, the regression analysis comprises: linear regression analysis, and/or GBDT regression analysis.
Wherein GBDT is Gradient Boosting Decision Tree and is translated into Gradient Boosting Decision Tree.
In one embodiment, the step-counting cheating regression model is used for performing regression analysis on the historical records of the user motion behavior tags and the user biological characteristic information tags and the user motion behavior tags and the user biological characteristic information tags extracted from the current step-counting data of the user to determine whether the user motion behavior tags and/or the user biological characteristic information tags in the current step-counting data of the user are abnormal.
In one embodiment, the ue status tag may include an analog location tag; the simulation positioning tag is used for describing whether the user equipment starts simulation positioning or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether the preset equipment is started in a simulated positioning mode or not and whether the equipment state is abnormal or not;
the device state analysis module is specifically configured to:
determining whether the user equipment starts simulated positioning according to the simulated positioning tag;
when the user equipment is determined to start the analog positioning, if the preset equipment state judgment condition is as follows: and when the equipment simulation positioning is started, determining that the state of the user equipment is abnormal if the corresponding equipment state is an abnormal state.
In one embodiment, the device status tag may include a connection power tag; the power connection label is used for describing whether the user equipment is connected with a power supply or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether a preset equipment connection power supply is started or not and whether the equipment state is abnormal or not;
the device state analysis module is specifically configured to:
determining whether the user equipment is connected with a power supply or not according to the power supply connection label;
when the user equipment is determined to be connected with the power supply, if the preset equipment state judgment condition is as follows: and when the equipment is connected with the power supply and the corresponding equipment state is an abnormal state, determining that the user equipment state is abnormal.
In one embodiment, the cheating result analysis module is specifically configured to:
if the analysis result of the step-counting cheating regression model shows that: the user motion behavior label/user biological characteristic information label in the current step counting data of the user is abnormal, and/or the analysis result of the user equipment state shows that: and if the user equipment state is abnormal, determining that the step counting process of the user is cheated.
In an embodiment, the step-counting cheating detection apparatus provided in an embodiment of the present invention, as shown in fig. 8, may further include:
an alarm module 06 for:
and when determining that the step counting process of the user is cheated, sending warning information of the cheating in the step counting process of the user.
In an embodiment, the step-counting cheating detection apparatus provided in an embodiment of the present invention may further include:
a recording module to:
and recording the analysis result of the step counting cheating regression model, the analysis result of the state of the user equipment and the result of determining whether the step counting process of the user is cheated or not to generate a record file.
In an embodiment, the step-counting cheating detection apparatus provided in an embodiment of the present invention may further include:
a model iterative optimization module to:
continuously extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from step counting data of a user;
and carrying out iterative optimization on the step-counting cheating regression model by using the continuously extracted user equipment state label, the user motion behavior label and the user biological characteristic information label.
A specific embodiment is given below to illustrate a specific application of the apparatus of the present invention, and in this embodiment, as shown in fig. 9, the apparatus may include:
firstly, a historical data acquisition module (namely, a step-counting cheating regression model module is established to carry out the operation of the acquired user historical step-counting data): the data acquisition module is responsible for acquiring data of users in historical walking activities;
a label extraction module (namely, a step-counting cheating regression model module is established to extract the user motion behavior label and the historical record of the user biological characteristic information label from the obtained user historical step counting data): the system is responsible for extracting an equipment state label (whether to plug in power, whether to turn on analog positioning and the like), a user behavior label (a user motion track, a user motion frequency, a user motion duration and the like), a biological label (biological feature information such as body temperature, heartbeat and the like in the user motion process) from a historical data center of a user;
thirdly, a step-counting cheating model building module (namely, a step-counting cheating regression model building module is established, and the machine learning algorithm is used for conducting regression analysis and modeling on the historical records of the user movement behavior tags and the user biological characteristic information tags to obtain the operation of the step-counting cheating regression model): learning the relation between the label and whether the user cheats through a modeling method of regression (logistic regression, GBDT and the like) and constructing a step-counting cheating model;
step counting cheating result prediction model module (namely user state analysis module) inputs the label of the user to the step counting cheating model to predict the cheating result of the user;
fifthly, a business rule input module (namely an equipment state analysis module): the "step-and-go" activity initiator may specify business rules (e.g., if the user turns on simulated positioning, the user is considered to cheat);
sixthly, a business rule result prediction module (namely an equipment state analysis module): inputting the label extracted from the label system into the service rule, and calculating the prediction result of the service rule;
seventhly, a result fusion module (namely a cheating result analysis module): the step counting cheating model is responsible for fusing the result predicted by the step counting cheating model with the result of the business rule;
eighthly, a result display module: and the fusion server is responsible for visually displaying the fused result.
Of course, it is to be understood that other variations of the above-described details may be made, and such variations are intended to fall within the scope of the present invention.
An embodiment of the present invention provides a computer device for implementing all or part of contents in the above step-counting cheating detection method, where the computer device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between related devices; the computer device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the computer device may be implemented with reference to the embodiment for implementing the step-counting cheating detection method and the embodiment for implementing the step-counting cheating detection apparatus in the embodiment, which are incorporated herein, and repeated details are not repeated herein.
Fig. 10 is a schematic block diagram of a system configuration of a computer apparatus 1000 according to an embodiment of the present application. As shown in fig. 10, the computer apparatus 1000 may include a central processing unit 1001 and a memory 1002; the memory 1002 is coupled to the cpu 1001. Notably, this fig. 10 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the step-counting cheating detection function may be integrated into the central processing unit 1001. The cpu 1001 may be configured to perform the following control:
extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user; the device state label is used for describing the state of the user device for recording the user pace counting data;
inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state;
and determining whether the step counting process of the user is cheated according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
In another embodiment, the step-counting cheating detection device may be configured separately from the central processing unit 1001, for example, the step-counting cheating detection device may be configured as a chip connected to the central processing unit 1001, and the step-counting cheating detection function is realized by the control of the central processing unit.
As shown in fig. 10, the computer apparatus 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, a power supply 1007. It is noted that the computer device 1000 does not necessarily include all of the components shown in FIG. 10; furthermore, the computer device 1000 may also comprise components not shown in fig. 10, which can be referred to in the prior art.
As shown in fig. 10, the central processing unit 1001, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, and the central processing unit 1001 receives input and controls the operation of the various components of the computer apparatus 1000.
The memory 1002 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the cpu 1001 can execute the program stored in the memory 1002 to realize information storage or processing, or the like.
The input unit 1004 provides input to the cpu 1001. The input unit 1004 is, for example, a key or a touch input device. The power supply 1007 is used to supply power to the computer apparatus 1000. The display 1006 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 1002 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 1002 may also be some other type of device. Memory 1002 includes buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application/function storage part 1022, the application/function storage part 1022 being used for storing application programs and function programs or a flow for executing the operation of the computer device 1000 by the central processing unit 1001.
The memory 1002 may also include a data store 1023, the data store 1023 being used to store data such as contacts, digital data, pictures, sounds and/or any other data used by the computer device. Driver storage 1024 of memory 1002 may include various drivers for the computer device for communication functions and/or for performing other functions of the computer device (e.g., messaging applications, directory applications, etc.).
The communication module 1003 is a transmitter/receiver 1003 that transmits and receives signals via an antenna 1008. A communication module (transmitter/receiver) 1003 is coupled to the central processor 1001 to provide an input signal and receive an output signal, which may be the same as the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 1003, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same computer device. The communication module (transmitter/receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010 to implement general telecommunications functions. The audio processor 1005 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 1005 is also coupled to the central processor 1001, so that sound can be recorded locally through the microphone 1010, and so that locally stored sound can be played through the speaker 1009.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for executing the step-counting cheating detection method. As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In the embodiment of the invention, a user equipment state label, a user motion behavior label and a user biological characteristic information label are extracted from the current step counting data of a user; the device state label is used for describing the state of the user device for recording the user pace counting data; inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user; comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state; whether the step-counting process of the user is cheated or not is determined according to the analysis result of the step-counting cheating regression model and the analysis result of the state of the user equipment, so that the state of the user and the state of the equipment can be analyzed through the extracted state tag of the user equipment, the state tag of the user movement behavior and the extracted biological characteristic information tag of the user, whether the step-counting process of the user is cheated or not is determined, step-counting cheating detection can be automatically performed, the situations that in the prior art, step-counting cheating detection needs to be performed manually, the detection result is inaccurate and a large amount of manpower and material resources are consumed are avoided, cheating detection does not need to be performed manually and depending on experience, manpower and material resources are saved, step-counting cheating detection is automatically achieved, and detection efficiency and detection accuracy of step-counting cheating detection are improved; meanwhile, the problem that in the prior art, the extra cost is increased due to the fact that a user needs to additionally wear and add hardware equipment for step counting cheating detection is solved, the user and a step counting activity initiator do not need to invest funds for the hardware equipment, manpower and material resources are saved, the problem that the user is inconvenient to move due to wearing of the hardware equipment is solved, the use experience of the user is improved, and the running activity initiator is favorable for popularizing activities.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (24)
1. A step-counting cheating detection method is characterized by comprising the following steps:
extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from the current step counting data of the user; the device state label is used for describing the state of the user device for recording user step counting data;
inputting the user movement behavior label and the user biological characteristic information label into a step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state;
and determining whether the step counting process of the user is cheated according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
2. The method of claim 1, wherein the user athletic performance tag comprises: labels respectively used for representing the motion track, the motion duration and the motion frequency of a user; the user biometric information tag includes: labels for characterizing the heartbeat and body temperature, respectively, of a user while exercising.
3. The method of claim 1, further comprising:
a step-counting cheating regression model is established as follows:
extracting historical records of the user movement behavior label and the user biological characteristic information label from the acquired user historical step counting data;
and performing regression analysis and modeling on the historical records of the user motion behavior tags and the user biological characteristic information tags by using a machine learning algorithm to obtain a step-counting cheating regression model.
4. The method of claim 3, wherein the regression analysis comprises: linear regression analysis, and/or GBDT regression analysis.
5. The method of claim 3, wherein the step-counting cheating regression model is used for performing regression analysis on the historical record of the user's athletic performance label and the user's biometric information label and the user's athletic performance label and the user's biometric information label extracted from the user's current step-counting data to determine whether the user's athletic performance label and/or the user's biometric information label are abnormal in the user's current step-counting data.
6. The method of claim 1, wherein the user equipment status tag comprises an analog location tag; the simulation positioning tag is used for describing whether the user equipment starts simulation positioning or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether the preset equipment is started in a simulated positioning mode or not and whether the equipment state is abnormal or not;
comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state, wherein the analysis result comprises the following steps:
determining whether the user equipment starts simulated positioning according to the simulated positioning tag;
when the user equipment is determined to start the analog positioning, if the preset equipment state judgment condition is as follows: and when the equipment simulation positioning is started, determining that the state of the user equipment is abnormal if the corresponding equipment state is an abnormal state.
7. The method of claim 1, wherein the device status tag comprises a connection power tag; the power connection label is used for describing whether the user equipment is connected with a power supply; the preset equipment state judgment condition is used for representing the corresponding relation between whether a preset equipment connection power supply is started or not and whether the equipment state is abnormal or not;
comparing and analyzing the user equipment state label with a preset equipment state judgment condition to obtain an analysis result of the user equipment state, wherein the analysis result comprises the following steps:
determining whether the user equipment is connected with a power supply or not according to the power supply connection label;
when the user equipment is determined to be connected with the power supply, if the preset equipment state judgment condition is as follows: and when the equipment is connected with the power supply and the corresponding equipment state is an abnormal state, determining that the user equipment state is abnormal.
8. The method of claim 1, wherein determining whether the user's step-counting process is cheating based on the results of the analysis of the step-counting cheating regression model and the results of the analysis of the user device status comprises:
if the analysis result of the step-counting cheating regression model shows that: the user motion behavior label/user biological characteristic information label in the current step counting data of the user is abnormal, and/or the analysis result of the user equipment state shows that: and if the user equipment state is abnormal, determining that the step counting process of the user is cheated.
9. The method of claim 1, further comprising:
and when determining that the step counting process of the user is cheated, sending warning information of the cheating in the step counting process of the user.
10. The method of claim 1, further comprising:
and recording the analysis result of the step counting cheating regression model, the analysis result of the state of the user equipment and the result of determining whether the step counting process of the user is cheated or not to generate a record file.
11. The method of claim 1, further comprising:
continuously extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from step counting data of a user;
and carrying out iterative optimization on the step-counting cheating regression model by using the continuously extracted user equipment state label, the user motion behavior label and the user biological characteristic information label.
12. A step-counting cheating detection device is characterized by comprising:
the system comprises a tag extraction module, a step counting module and a step counting module, wherein the tag extraction module is used for extracting a user equipment state tag, a user motion behavior tag and a user biological characteristic information tag from current step counting data of a user; the device state label is used for describing the state of the user device for recording user step counting data;
the user state analysis module is used for inputting the user motion behavior label and the user biological characteristic information label into the step-counting cheating regression model to obtain an analysis result of the step-counting cheating regression model on the user state; the step-counting cheating regression model is obtained by performing regression analysis and modeling on historical step-counting data of the user;
the equipment state analysis module is used for comparing and analyzing the user equipment state label with preset equipment state judgment conditions to obtain an analysis result of the user equipment state;
and the cheating result analysis module is used for determining whether the step counting process of the user cheats according to the analysis result of the step counting cheating regression model and the analysis result of the state of the user equipment.
13. The apparatus of claim 12, wherein the user athletic performance tag comprises: labels respectively used for representing the motion track, the motion duration and the motion frequency of a user; the user biometric information tag includes: labels for characterizing the heartbeat and body temperature, respectively, of a user while exercising.
14. The apparatus of claim 12, further comprising:
establishing a step-counting cheating regression model module for:
a step-counting cheating regression model is established as follows:
extracting historical records of the user movement behavior label and the user biological characteristic information label from the acquired user historical step counting data;
and performing regression analysis and modeling on the historical records of the user motion behavior tags and the user biological characteristic information tags by using a machine learning algorithm to obtain a step-counting cheating regression model.
15. The apparatus of claim 14, wherein the regression analysis comprises: linear regression analysis, and/or GBDT regression analysis.
16. The apparatus of claim 14, wherein the step-counting cheating regression model is configured to perform regression analysis on the historical record of the user athletic performance signature and the user biometric information signature and the user athletic performance signature and the user biometric information signature extracted from the current step-counting data of the user to determine whether the user athletic performance signature and/or the user biometric information signature are abnormal in the current step-counting data of the user.
17. The apparatus of claim 12, wherein the user equipment status tag comprises an analog location tag; the simulation positioning tag is used for describing whether the user equipment starts simulation positioning or not; the preset equipment state judgment condition is used for representing the corresponding relation between whether the preset equipment is started in a simulated positioning mode or not and whether the equipment state is abnormal or not;
the device state analysis module is specifically configured to:
determining whether the user equipment starts simulated positioning according to the simulated positioning tag;
when the user equipment is determined to start the analog positioning, if the preset equipment state judgment condition is as follows: and when the equipment simulation positioning is started, determining that the state of the user equipment is abnormal if the corresponding equipment state is an abnormal state.
18. The apparatus of claim 12, wherein the device status tag comprises a connection power tag; the power connection label is used for describing whether the user equipment is connected with a power supply; the preset equipment state judgment condition is used for representing the corresponding relation between whether a preset equipment connection power supply is started or not and whether the equipment state is abnormal or not;
the device state analysis module is specifically configured to:
determining whether the user equipment is connected with a power supply or not according to the power supply connection label;
when the user equipment is determined to be connected with the power supply, if the preset equipment state judgment condition is as follows: and when the equipment is connected with the power supply and the corresponding equipment state is an abnormal state, determining that the user equipment state is abnormal.
19. The apparatus of claim 12, wherein the cheating-result analysis module is specifically configured to:
if the analysis result of the step-counting cheating regression model shows that: the user motion behavior label/user biological characteristic information label in the current step counting data of the user is abnormal, and/or the analysis result of the user equipment state shows that: and if the user equipment state is abnormal, determining that the step counting process of the user is cheated.
20. The apparatus of claim 12, further comprising:
an alarm module for:
and when determining that the step counting process of the user is cheated, sending warning information of the cheating in the step counting process of the user.
21. The apparatus of claim 12, further comprising:
a recording module to:
and recording the analysis result of the step counting cheating regression model, the analysis result of the state of the user equipment and the result of determining whether the step counting process of the user is cheated or not to generate a record file.
22. The apparatus of claim 12, further comprising:
a model iterative optimization module to:
continuously extracting a user equipment state label, a user motion behavior label and a user biological characteristic information label from step counting data of a user;
and carrying out iterative optimization on the step-counting cheating regression model by using the continuously extracted user equipment state label, the user motion behavior label and the user biological characteristic information label.
23. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 11 when executing the computer program.
24. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 11.
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