Black production data identification method and device, equipment and storage medium

Abstract

The present disclosure provides a black production data identification method and device, equipment and storage medium, relates to the technical field of computers, specifically relates to the technical field of data analysis, data processing and the like, and can be applied to scenes such as black production data analysis and black production data identification. The specific implementation scheme comprises: obtaining touch data corresponding to a touch panel in a preset time period; determining the equidistant average curve sliding curvature of the touch sequence according to the first parameter of the data points in the touch sequence; determining the first target parameter of the target region according to the number of data points in the target region and the number of data points in the touch panel; summing the target parameters of all target regions according to a preset weight to determine the convolution weight information entropy of the touch panel; and identifying whether the touch data is black production data according to the equidistant average curve sliding curvature and the convolution weight information entropy. The present disclosure can improve the identification efficiency of black production data.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer technology, in particular to the technical field of data analysis, data processing and the like, which can be applied to the scene of black production data analysis, black production data identification and the like, and in particular to a black production data identification method, device and equipment and storage medium. BACKGROUND

[0002] Robot process automation refers to the use of software robots to simulate human sliding or clicking on a touch panel, using various technologies to reduce repetitive and tedious work, and realizing business process automation of robot software. However, with the update of technology, robot process automation is applied in different scenes, such as automatic red envelope grabbing, automatic chat and other black and gray production scenes, forming a lot of black production data. In order to protect the data security of users, it is necessary to identify the black production data.

[0003] At present, the way to identify black production data is to identify the frequency of clicking and sliding in the data obtained from the touch panel, and then determine whether the obtained data is black production data. Alternatively, a neural network is used to identify whether the obtained data is black production data.

[0004] However, the above two ways of identifying black production data have low accuracy. SUMMARY

[0005] The present disclosure provides a black production data identification method, device, equipment and storage medium, which can improve the identification efficiency of black production data.

[0006] According to a first aspect of the present disclosure, a black production data identification method is provided, the method comprising: obtaining touch data corresponding to a touch panel in a preset time period, the touch data comprising at least one touch sequence, each touch sequence being formed by sliding or clicking on the surface of the touch panel, the touch sequence being composed of at least one data point, each data point comprising a first parameter, the first parameter being used to indicate the position of each data point in the touch panel, the touch panel comprising at least two target regions; determining the equidistant average curve sliding curvature of the touch sequence according to the first parameter of the data point in the touch sequence; determining the first target parameter of the target region according to the number of data points in the target region and the number of data points in the touch panel; summing the target parameters of all target regions according to a preset weight to determine the convolution weight information entropy of the touch panel; and identifying whether the touch data is black production data according to the equidistant average curve sliding curvature and the convolution weight information entropy.

[0007] According to a second aspect of the present disclosure, a black production data identification device is provided, the device comprising: an obtaining unit, an identification unit.

[0008] The acquisition unit is configured to acquire touch data corresponding to the touch panel in a preset time period, the touch data comprising at least one touch sequence, each touch sequence being formed by sliding or clicking on the surface of the touch panel, the touch sequence comprising at least one data point, each data point comprising a first parameter, the first parameter being used to indicate the position of each data point in the touch panel, the touch panel comprising at least two target regions; determining the equidistant average curve sliding curvature of the touch sequence according to the first parameter of the data point in the touch sequence; determining the first target parameter of the target region according to the number of data points in the target region and the number of data points in the touch panel; determining the convolution weight information entropy of the touch panel by weighting and summing the target parameters of all target regions according to a preset weight.

[0009] The identification unit is configured to identify whether the touch data is black production data according to the equidistant average curve sliding curvature and the convolution weight information entropy.

[0010] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

[0011] According to a fourth aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, the computer instructions being used to enable a computer to perform the method according to the first aspect.

[0012] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, the computer program being used to implement the method according to the first aspect when executed by a processor.

[0013] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings are used to better understand the present scheme, and do not constitute a limitation on the present disclosure. Among them:

[0015] Figure 1 A flowchart of a black production data identification method provided by an embodiment of the present disclosure is provided;

[0016] Figure 2 Another flowchart of a black production data identification method provided by an embodiment of the present disclosure is provided;

[0017] Figure 3 Still another flowchart of a black production data identification method provided by an embodiment of the present disclosure is provided;

[0018] Figure 4 Another flowchart of a method for identifying black production data according to an embodiment of the present disclosure is shown in FIG. 6.

[0019] Figure 5 A schematic diagram of a device for identifying black production data according to an embodiment of the present disclosure is shown in FIG. 7.

[0020] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown in FIG. 8. DETAILED DESCRIPTION

[0021] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to help the understanding of the present disclosure. These should be considered in their context only. Thus, those of ordinary skill in the art will recognize the various changes and modifications of the embodiments described herein, without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for the sake of clarity and conciseness.

[0022] It should be understood that, in the embodiments of the present disclosure, the character " / " generally represents that the associated objects before and after are in an "or" relationship. The terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated.

[0023] Robotic process automation refers to a kind of robot software that uses software robots to simulate people sliding or clicking on touch panels, uses various technologies to reduce repetitive and tedious work, and realizes business process automation. However, with the update of technology, robotic process automation is applied in different scenarios, such as automatic red envelope grabbing, automatic chatting and other black production scenarios, forming a lot of black production data. In order to protect the data security of users, it is necessary to identify the black production data.

[0024] Illustratively, black production data can endanger the data security of users, so in order to protect the data security of users, it is necessary to identify the black production data.

[0025] At present, the way to identify black production data is: by identifying the frequency of clicking and sliding in the data obtained from the touch panel, and then judging whether the obtained data is black production data. Or, by using a neural network to identify whether the obtained data is black production data.

[0026] For example, the frequency of the obtained data is calculated, and it is judged whether the obtained data is black production data according to the calculation result. Alternatively, the obtained data can be input into a preset data recognition model, and an identification result of the obtained data is output by the preset data recognition model, and it is judged whether the obtained data is black production data according to the identification result.

[0027] However, the above two ways of identifying black production data have low accuracy.

[0028] For example, the frequency of the obtained data is calculated by analyzing the frequency of the obtained data. The generation frequency of the data is calculated, and the characteristics of the data formed by the user clicking or sliding on the touch panel are irrelevant, that is, the data formed by the user sliding or clicking cannot be presented, and the accuracy of identifying black production data is low. The output result of the preset data recognition model on the touch data does not have a corresponding reason to support the identification result, and the interpretability of the touch data is weak.

[0029] In this background, the present disclosure provides a black production data identification method, which can improve the identification efficiency of black production data.

[0030] In some possible examples, the black production data can also be referred to as abnormal data, cheating data, non-normal data, etc.

[0031] For example, the execution subject of the black production data identification method can be a computer or a server, or can also be other devices with data processing capability. The execution subject of the method is not limited herein.

[0032] In some embodiments, the server can be a single server, or can also be a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. The specific implementation of the server is not limited in the present disclosure.

[0033] Figure 1 The flowchart of the black production data identification method provided by the embodiments of the present disclosure is shown in FIG. 1. Figure 1 As shown in FIG. 1, the method can include steps S101-S105.

[0034] S101, obtaining touch data corresponding to a touch panel in a preset time period, the touch data including at least one touch sequence, each touch sequence being formed by sliding or clicking on the surface of the touch panel, the touch sequence being composed of at least one data point, each data point including a first parameter, the first parameter being used to indicate the position of each data point in the touch panel, and the touch panel including at least two target regions.

[0035] Exemplarily, the preset time period can be set according to actual conditions, and the preset time period is not specifically limited herein. A midpoint of the touch panel can be taken as an origin, a direction horizontally to the right can be taken as a positive direction of an x-axis, and a direction vertically upward to the x-axis can be taken as a positive direction of a y-axis to establish a plane rectangular coordinate system. A position of each data point in the plane rectangular coordinate system can be determined through the established rectangular coordinate system. The first parameter can be in a form of (x, y), x can be used to represent a horizontal coordinate of the data point in the touch panel, and y can be used to represent a vertical coordinate of the data point in the touch panel.

[0036] The target region can be obtained by dividing the touch panel, and the target region can be divided at least once to obtain at least two target regions.

[0037] For example, the preset time period can be from 8:00 am to 12:00 pm every day. The obtained touch data can include two touch sequences. The first touch sequence can include three data points, and the first parameters of each data point are respectively (x1, y1), (x2, y2), and (x3, y3). The second touch sequence can include five data points. The touch panel can be divided into four target regions with equal areas through equal-area division.

[0038] In S102, the equal-distance average curve sliding curvature of the touch data is determined according to the first parameters of the data points in the touch sequence.

[0039] Exemplarily, the calculation manner of the equal-distance average curve sliding curvature of each touch sequence can be determined according to the first parameters of the data points in each touch sequence, and the calculation manner can be as follows:

[0040] Formula (1)

[0041] In Formula (1), ave represents the equal-distance average curve sliding curvature of the current touch sequence, N represents the number of data points in the current touch sequence, j represents the jth data point in the current touch sequence, represents the first parameter of the jth data point in the current touch sequence, represents the Euclidean distance between the j+1th data point and the jth data point in the current touch sequence, represents a vector formed by the jth data point and the j-1th data point in the current touch sequence, represents a vector formed by the j+1th data point and the jth data point in the current touch sequence, represents a cosine distance between the vector formed by the jth data point and the j-1th data point in the current touch sequence and the vector formed by the j+1th data point and the jth data point in the current touch sequence.

[0042] For example, the first touch sequence can include 3 data points, and the first parameters of each data point are respectively (x1, y1), (x2, y2), and (x3, y3). The first parameters of the three data points can be input into formula (1), and the equal-distance average curve sliding curvature of the first touch sequence is calculated by formula (1). The equal-distance average curve sliding curvatures of all touch sequences are summed and averaged to determine the equal-distance average curve sliding curvature of the touch data. For example, the equal-distance average curve sliding curvature of the touch data can be 0.5.

[0043] S103, determining a first target parameter of the target region according to the number of data points in the target region and the number of data points in the touch panel.

[0044] For example, before calculating the first target parameter of the target region, the target region can be divided into a plurality of sub-regions. The calculation formula of the first target parameter of the target region according to the number of data points in the target region and the number of data points in the touch panel can be:

[0045] Formula (2)

[0046] In formula (2), represents the first target parameter of the i-th target region, b represents the b-th sub-region of the target region, M represents the total number of sub-regions in the target region, represents the ratio of the number of data points in the b-th sub-region to the number of data points in the target region.

[0047] For example, the target region can be divided into 4 sub-regions, each of which includes c, d, e, and f data points. In formula (2) above, the value of M is 4, The values of c / (c+d+e+f), d / (c+d+e+f), e / (c+d+e+f), and f / (c+d+e+f) are respectively c, d, e, and f. The determined first target parameter of the target region can be 2.

[0048] S104, summing and weighting all the first target parameters of the target regions according to a preset weight to determine the convolution weight information entropy of the touch data.

[0049] For example, the preset weight can be set by the user or can be set according to actual conditions, and the size of the preset weight is not limited here. The calculation formula of the convolution weight information entropy of the touch data by summing and weighting all the first target parameters of the target regions according to a preset weight can be:

[0050] Formula (3)

[0051] In formula (3), conv represents the convolution weight information entropy of the touch data, i represents the i-th target region, and N represents the number of all target regions. The first target parameter of the i-th target region is represented by g. The preset weight corresponding to the i-th target region is represented by w.

[0052] For example, the number of target regions in the touch panel can be 2, N in formula (3) is 2, the first target parameters of the first target region and the second target region can be g and l respectively, and the preset weights corresponding to the first target region and the second target region can be 0.5 and 0.5 respectively. According to formula (3), the convolution weight information entropy of the touch data can be 5.

[0053] In S105, whether the touch data is black production data is identified according to the equal-distance average curve sliding curvature and the convolution weight information entropy.

[0054] For example, the equal-distance average curve sliding curvature of the touch data can be 1, and the convolution weight information entropy of the touch data can be 5. The equal-distance average curve sliding curvature and the convolution weight information entropy are added and averaged to obtain 3. Whether the touch data is black production data can be identified by comparing 3 with a preset threshold.

[0055] For example, the equal-distance average curve sliding curvature of the touch data can be 1, and the convolution weight information entropy of the touch data can be 5. The equal-distance average curve sliding curvature and the convolution weight information entropy are added and averaged to obtain 3. Whether the touch data is black production data can be identified by comparing 3 with a preset threshold.

[0056] The present disclosure can obtain the touch data corresponding to the touch panel in a preset time period, determine the equal-distance average curve sliding curvature of the touch data according to the first parameter of the data points in the touch sequence, convert the features of the data points in the touch sequence into the equal-distance average curve sliding curvature and quantify, and reflect the data features of the touch data through the equal-distance average curve sliding curvature, thereby enhancing the identification accuracy of the touch data. The first target parameter of the target region can be determined according to the number of data points in the target region and the number of data points in the touch panel, and the position features of the data points can be quantified. The target parameters of all target regions are weighted and summed according to the preset weight to determine the convolution weight information entropy of the touch data. According to the equal-distance average curve sliding curvature and the convolution weight information entropy, whether the touch data is black production data can be identified, which can improve the identification efficiency of the black production data, protect the user data security, and improve the user experience.

[0057] In some embodiments, Figure 1Before step S101, the black production data identification method further includes: according to a preset partition rule, dividing the touch panel into at least two target areas.

[0058] For example, the preset partition rule can be set according to actual conditions, and the preset partition rule is not limited here.

[0059] For example, the preset partition rule can be to divide the touch panel into 9 target areas by equal area.

[0060] In this embodiment, the touch panel can be divided more finely according to the preset partition rule, and the position of the data point can be divided more finely, and the efficiency of data identification can be optimized by determining the data distribution characteristics of each target area.

[0061] In some embodiments, before the touch panel is divided into at least two target areas according to the preset partition rule, the black production data identification method further includes: determining the preset partition rule according to a first initial parameter set by a user.

[0062] For example, the first initial parameter set by the user can be 9, and the determined preset partition rule can be to divide the touch panel into 9 target areas by equal area.

[0063] For example, the first initial parameter set by the user can be 4, and the determined preset partition rule can be to divide the touch panel into 4 target areas by equal area.

[0064] In this embodiment, the preset partition rule is determined according to the first initial parameter set by the user, so as to realize customized division of the touch panel according to the first initial parameter set by the user, and improve user experience.

[0065] Figure 2 Another flowchart of the black production data identification method provided by the embodiment of the present disclosure is provided. As shown in Figure 2 some embodiments, Figure 1 Before step S101, the black production data identification method further includes steps S201-S202.

[0066] S201, obtain size information of the touch panel.

[0067] For example, the touch panel can be rectangular, and the obtained size information of the touch panel can be 2:1 for length:width; or square, and the obtained size information of the touch panel can be 1:1 for length:width.

[0068] S202, determine a preset partition rule according to the size information.

[0069] Exemplarily, the size information of the acquired touch panel can be taken as an example in which the length:width is equal to 2:1. According to the size information of the touch panel, the preset partition rule determined can be that the size information of all target regions after partitioning is also equal to 2:1.

[0070] Optionally, the size information of the acquired touch panel can be taken as an example in which the length:width is equal to 2:1, and the first initial parameter preset by the user can be 8. According to the size information of the touch panel and the first initial parameter preset, the preset partition rule determined can be that the touch panel is divided into 8 target regions with equal areas, and the size information of each region is equal to 2:1.

[0071] In some examples, the preset partition rule can be determined according to the size information of the touch panel and / or the actual situation of the first initial parameter preset, which is not limited herein.

[0072] The embodiment can provide diversified implementation manners for determining the preset partition rule of the touch panel by acquiring the size information of the touch panel and determining the preset partition rule according to the size information. Different implementation manners can meet different needs of users and adapt to different size information of the touch panel, provide diversified partition manners, and thus the touch data can be divided and analyzed from multiple angles, and the diversity of touch data analysis is improved.

[0073] In some embodiments, Figure 1 The step S102 can include determining the equal-distance average curve sliding curvature of the touch data according to the first parameter of all data points in the touch sequence.

[0074] Exemplarily, the touch sequence can include 100 data points. The first parameter of the 100 data points in the touch sequence can be used to determine the equal-distance average curve sliding curvature of the touch data by using formula (1).

[0075] The embodiment can determine the equal-distance average curve sliding curvature of the touch data according to the first parameter of all data points in the touch sequence. By analyzing all data points, the data characteristics of all data points can be covered, and the accuracy of touch data analysis is improved.

[0076] Figure 3 Another flowchart of the black production data identification method provided by the embodiment of the present disclosure is shown in FIG. 6. Figure 3 As shown in some embodiments, Figure 1 The step S102 can include steps S301-S302.

[0077] S301, filtering the data points in the touch sequence according to a preset rule to obtain first data points.

[0078] S302, determine the equal-distance average curve sliding curvature of the touch data according to the first parameter of the first data point.

[0079] Exemplarily, the preset rule can be set according to actual conditions, and the preset rule is not limited herein.

[0080] For example, the touch sequence can contain 100 data points, and the preset rule can be that the number of data points between adjacent first data points is 1. According to the preset rule, the data points in each touch sequence can be filtered to obtain 50 first data points. According to the first parameters of the 50 filtered data points and formulas (2) and (3), the equal-distance average curve sliding curvature of the touch data is determined.

[0081] The embodiment filters the data points in the touch sequence according to the preset rule to obtain the first data points, can filter the data points according to the preset rule, is more in line with the data characteristics of the data points in the preset rule, determines the equal-distance average curve sliding curvature of the touch data according to the first parameters of the first data points, and can form a new touch sequence from the first data points of all the data points. Different touch sequences can cover different sliding speeds and sliding habits of users, and further improve the accuracy of touch data analysis.

[0082] In some embodiments, Figure 3 Before step S301, the black production data identification method further includes: determining a preset rule according to a second initial parameter set by a user.

[0083] For example, the second initial parameter set by the user can be 2, and the determined preset rule can be that the number of data points between adjacent first data points is 2. For another example, the second initial parameter set by the user can be 3, and the determined preset rule can be that the number of data points between adjacent first data points is 3.

[0084] In some examples, the user can use a corresponding data analysis model to obtain all data points in the touch sequence, use different preset rules to filter all data points through the data analysis model, and output the equal-distance average curve sliding curvature calculated by the first data points determined by different filtering rules. The preset rule is determined by manually analyzing the equal-distance average curve sliding curvature, and then the second initial parameter is determined through the preset rule. The way in which the user obtains the second initial parameter is not limited herein.

[0085] The embodiment determines the preset rule according to the second initial parameter set by the user, can make the determined preset rule more in line with the requirements of the user, improve the user experience, and screen out the first data point through the preset rule set by the user, and determine the equal-distance average curve sliding curvature through the screened first data point, which can further fit the habits of the user's sliding or clicking, and further improve the accuracy of touch data analysis.

[0086] In some embodiments, Figure 1 Before step S104, the black production data identification method further includes: determining a preset weight of the target area through a preset weight calculation model.

[0087] For example, the preset weight calculation model can use the number of data points in the target area as input and the weight corresponding to the target area as output, and is obtained by training a neural network. The implementation of the preset weight calculation model is not limited here.

[0088] The embodiment determines the preset weight of the target area through the preset weight calculation model, which can improve the flexibility and accuracy of the target area weight value and further improve the accuracy of touch data analysis.

[0089] Figure 4 Another flowchart of the black production data identification method provided by the embodiment of the present disclosure is shown in FIG. 5. Figure 4 As shown in some embodiments, Figure 1 Step S105 can include steps S401-S403.

[0090] S401, determining a target parameter of the touch data according to the equal-distance average curve sliding curvature and the convolution weight information entropy.

[0091] For example, the target parameter can be obtained by taking the Cartesian product of the equal-distance average curve sliding curvature and the convolution weight information entropy, or can be obtained by weighting and averaging the equal-distance average curve sliding curvature and the convolution weight information entropy. The way of determining the target parameter of the touch data according to the equal-distance average curve sliding curvature and the convolution weight information entropy is not limited here.

[0092] For example, the target parameter determined according to the equal-distance average curve sliding curvature and the convolution weight information entropy can be 5.

[0093] S402, when the target parameter is greater than a preset parameter threshold, determining that the touch data is black production data.

[0094] S403, when the target parameter is less than the preset parameter threshold, determining that the touch data is non-black production data.

[0095] Exemplarily, the preset parameter threshold can be set according to actual conditions, and the preset parameter threshold is not limited herein.

[0096] For example, the preset parameter threshold can be set as 4. The target parameter of the touch data can be 5, and it can be determined that the touch data is black production data.

[0097] For another example, the preset parameter threshold can be set as 6. The target parameter of the touch data can be 5, and it can be determined that the touch data is non-black production data.

[0098] In this embodiment, the target parameter of the touch data is determined according to the equal-distance average curve sliding curvature and the convolution weight information entropy. The characteristics of the touch data can be quantified through the target parameter, and whether the touch data is black production data can be determined according to the target parameter and the preset parameter threshold. The accuracy of analyzing the touch data can be further improved through the quantification of the data characteristics.

[0099] In the exemplary embodiments, the disclosure also provides a black production data identification device, which can be used to implement the black production data identification method as described in the foregoing embodiments. Figure 5 As shown in the constituent schematic diagram of the black production data identification device provided by the disclosure, Figure 5 The device can include an acquisition unit 501 and an identification unit 502.

[0100] The acquisition unit 501 is configured to acquire touch data corresponding to a touch panel in a preset time period. The touch data includes at least one touch sequence. Each touch sequence is formed by sliding or clicking on the surface of the touch panel. The touch sequence is composed of at least one data point. Each data point includes a first parameter, which is used to indicate the position of each data point in the touch panel. The touch panel includes at least two target regions. The equal-distance average curve sliding curvature of the touch sequence is determined according to the first parameter of the data point in the touch sequence. The first target parameter of the target region is determined according to the number of data points in the target region and the number of data points in the touch panel. The convolution weight information entropy of the touch panel is determined by weighting and summing the target parameters of all target regions according to a preset weight.

[0101] The identification unit 502 is configured to identify whether the touch data is black production data according to the equal-distance average curve sliding curvature and the convolution weight information entropy.

[0102] Optionally, before acquiring the touch data corresponding to the touch panel in the preset time period, the acquisition unit 501 is further configured to divide the touch panel into at least two target regions according to a preset partition rule.

[0103] Optionally, before the touch panel is divided into the at least two target regions according to the preset partition rule, the obtaining unit 501 is further configured to determine the preset partition rule according to a first initial parameter set by a user.

[0104] Optionally, the obtaining unit 501 is specifically configured to determine the equal-distance average curve sliding curvature of the touch sequence according to the first parameter of all data points in the touch sequence.

[0105] Optionally, the obtaining unit 501 is specifically configured to: filter the data points in the touch sequence according to a preset rule to obtain the first data point; and determine the equal-distance average curve sliding curvature of the touch sequence according to the first parameter of the first data point.

[0106] Optionally, before the data points in the touch sequence are filtered according to the preset rule, the obtaining unit 501 is further configured to determine the preset rule according to a second initial parameter set by a user.

[0107] Optionally, before the convolution weight information entropy of the touch panel is determined by summing up the target parameters of all target regions according to the preset weight, the obtaining unit 501 is further configured to determine the preset weight of the target region by using a preset weight calculation model.

[0108] Optionally, the identifying unit 502 is specifically configured to: determine the target parameter of the touch data according to the equal-distance average curve sliding curvature and the convolution weight information entropy; determine that the touch data is black production data when the target parameter is greater than a preset parameter threshold; and determine that the touch data is non-black production data when the target parameter is less than the preset parameter threshold.

[0109] In the technical solutions of the present disclosure, the acquisition, storage and application of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0110] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0111] In an example embodiment, the electronic device comprises at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method as described in the above embodiments.

[0112] In an example embodiment, the readable storage medium can be a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to the above embodiments.

[0113] In an exemplary embodiment, the computer program product includes a computer program that, when executed by a processor, implements the method described in the above embodiments.

[0114] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0115] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0116] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 601 performs various methods and processes described above, such as the black production data identification method. For example, in some embodiments, the black production data identification method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded onto the RAM 603 and executed by the computing unit 601, one or more steps of the black production data identification method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the black production data identification by other any appropriate means, such as by means of firmware. Various embodiments of the systems and techniques described herein above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0118] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0119] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0121] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0122] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0123] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technology disclosed in the present disclosure can be achieved, which is not limited herein.

[0124] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for identifying black production data, the method comprising: obtaining touch data corresponding to a touch panel in a preset time period, the touch data comprising at least one touch sequence, each touch sequence being formed by sliding or clicking on a surface of the touch panel, each touch sequence comprising at least one data point, each data point comprising a first parameter, the first parameter being used to indicate a position of each data point in the touch panel, the touch panel comprising at least two target regions; determining an equidistant average curve sliding curvature of the touch data according to the first parameter of the data points in the touch sequence, wherein the equidistant average curve sliding curvature value of a single touch sequence is calculated by the following formula: ； wherein ave represents the equidistance average curve sliding curvature of the current touch sequence, N represents the number of data points in the current touch sequence, j represents the jth data point in the current touch sequence, represents the first parameter of the jth data point in the current touch sequence, represents the Euclidean distance between the jth data point and the j+1th data point in the current touch sequence, represents the vector formed by the jth data point and the j-1th data point in the current touch sequence, represents the vector formed by the jth data point and the j+1th data point in the current touch sequence, represents the cosine distance between the vector formed by the jth data point and the j-1th data point in the current touch sequence and the vector formed by the jth data point and the j+1th data point in the current touch sequence. determining a first target parameter of the target regions according to a number of the data points in the target regions and a number of the data points in the touch panel; determining a convolution weight information entropy of the touch data by weighted sum of the first target parameters of all the target regions according to a preset weight; identifying whether the touch data is black production data according to the equidistant average curve sliding curvature and the convolution weight information entropy. 2.The method of claim 1, before the obtaining touch data corresponding to a touch panel in a preset time period, the method further comprising: dividing the touch panel into at least two target regions according to a preset partition rule. 3.The method of claim 2, before the dividing the touch panel into at least two target regions according to a preset partition rule, the method further comprising: determining the preset partition rule according to a first initial parameter set by a user. 4.The method of claim 2, before the dividing the touch panel into at least two target regions according to a preset partition rule, the method further comprising: obtaining size information of the touch panel; determining the preset partition rule according to the size information. 5.The method of any one of claims 1-4, wherein the determining an equidistant average curve sliding curvature of the touch data according to the first parameter of the data points in the touch sequence comprises: determining the equidistant average curve sliding curvature of the touch data according to the first parameter of all the data points in the touch sequence. 6.The method of any one of claims 1-4, wherein the determining an equidistant average curve sliding curvature of the touch data according to the first parameter of the data points in the touch sequence comprises: screening the data points in the touch sequence according to a preset rule to obtain first data points; determining the equidistant average curve sliding curvature of the touch data according to the first parameter of the first data points. 7.The method of claim 6, before the screening the data points in the touch sequence according to a preset rule, the method further comprising: determining the preset rule according to a second initial parameter set by a user.

8. The method of any one of claims 1-4, before the determining the convolution weight information entropy of the touch data by summing up target parameters of all the target regions according to preset weights, the method further comprising: determining preset weights of the target regions by a preset weight calculation model.

9. The method of any one of claims 1-4, the identifying whether the touch data is black production data according to the equal-distance average curve sliding curvature and the convolution weight information entropy, comprising: determining a target parameter of the touch data according to the equal-distance average curve sliding curvature and the convolution weight information entropy; when the target parameter is greater than a preset parameter threshold, determining that the touch data is black production data; when the target parameter is less than the preset parameter threshold, determining that the touch data is non-black production data.

10. A black production data apparatus, the apparatus comprising: an acquisition unit, configured to acquire touch data corresponding to a touch panel in a preset time period, the touch data comprising at least one touch sequence, each touch sequence being formed by sliding or clicking on a surface of the touch panel, each touch sequence comprising at least one data point, and each data point comprising a first parameter, the first parameter being used to indicate a position of each data point in the touch panel, and the touch panel comprising at least two target regions; determining an equal-distance average curve sliding curvature of each touch sequence according to the first parameter of the data point in the touch sequence, and determining a first target parameter of each target region according to a number of the data points in the target region and a number of the data points in the touch panel; summing up target parameters of all the target regions according to preset weights to determine a convolution weight information entropy of the touch panel; wherein the equal-distance average curve sliding curvature value of a single touch sequence is calculated by the following formula: ； wherein ave represents the equidistance average curve sliding curvature of the current touch sequence, N represents the number of data points in the current touch sequence, j represents the jth data point in the current touch sequence, represents the first parameter of the jth data point in the current touch sequence, represents the Euclidean distance between the jth data point and the j+1th data point in the current touch sequence, represents the vector formed by the jth data point and the j-1th data point in the current touch sequence, represents the vector formed by the jth data point and the j+1th data point in the current touch sequence, represents the cosine distance between the vector formed by the jth data point and the j-1th data point in the current touch sequence and the vector formed by the jth data point and the j+1th data point in the current touch sequence. an identification unit, configured to identify whether the touch data is black production data according to the equal-distance average curve sliding curvature and the convolution weight information entropy.

11. The apparatus of claim 10, before the acquisition unit acquires the touch data corresponding to the touch panel in the preset time period, the acquisition unit is further configured to: divide the touch panel into the at least two target regions according to a preset partition rule.

12. The apparatus of claim 11, before the acquisition unit divides the touch panel into the at least two target regions according to the preset partition rule, the acquisition unit is further configured to: determine the preset partition rule according to a first initial parameter set by a user.

13. The apparatus of claim 11, before the acquisition unit divides the touch panel into the at least two target regions according to the preset partition rule, the acquisition unit is further configured to: acquire size information of the touch panel; determine the preset partition rule according to the size information.

14. The apparatus of any one of claims 10-13, the acquisition unit is specifically configured to: determine the equal-distance average curve sliding curvature of each touch sequence according to the first parameter of all the data points in the touch sequence.

15. The apparatus of any one of claims 10-13, the obtaining unit is specifically configured to: filter the data points in the touch sequence according to a preset rule to obtain first data points; determine an equidistant average curve sliding curvature of the touch sequence according to a first parameter of the first data points.

16. The apparatus of claim 15, before the filtering the data points in the touch sequence according to a preset rule, the obtaining unit is further configured to: determine the preset rule according to a second initial parameter set by a user.

17. The apparatus of any one of claims 10-13, before the determining the convolution weight information entropy of the touch panel by summing up the target parameters of all the target areas according to a preset weight, the obtaining unit is further configured to: determine a preset weight of the target area by a preset weight calculation model.

18. The apparatus of any one of claims 10-13, the identifying unit is specifically configured to: determine a target parameter of the touch data according to the equidistant average curve sliding curvature and the convolution weight information entropy; determine that the touch data is black production data when the target parameter is greater than a preset parameter threshold; determine that the touch data is non-black production data when the target parameter is less than the preset parameter threshold.

19. An electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

20. A non-transitory computer-readable storage medium having computer instructions stored therein, the computer instructions being used to cause a computer to perform the method of any one of claims 1-9.

21. A computer program product comprising a computer program, the computer program being implemented to perform the method of any one of claims 1-9 when executed by a processor.

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