A handwriting data distribution method and system based on big data

Abstract

The present invention provides a handwriting data distribution method and system based on big data, wherein the method is applied to a data distribution node, including: step S1: obtaining a handwriting data acquisition request from a client; step S2: parsing the handwriting data acquisition request, and determining information of target data corresponding to the handwriting data acquisition request; step S3: querying a preset storage path library, and determining a data storage node corresponding to the target data; step S4: generating connection information and verification information of the data storage node based on node information of the pre-stored data storage node; step S5: sending the connection information to the client, and sending the verification information to the data storage node. The handwriting data distribution method based on big data of the present invention distributes handwriting data in a coordinated manner based on data distribution nodes, and the data storage nodes complete the distribution of handwriting data, thereby reducing the load of distribution by the data storage nodes alone, and improving the efficiency of handwriting data distribution by the data storage nodes.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a handwriting data distribution method and system based on big data. Background Art

[0002] At present, with the development of computer technology and the advancement of network technology, human work and life have undergone tremendous changes. Now people are exposed to, read, distribute and process information every day, such as social interaction, news, knowledge elements, shopping, entertainment, etc. through mobile terminals, which makes the amount of data created increase exponentially. The massive data thus formed is called big data.

[0003] When a user logs in using handwriting, a big data platform is required to distribute the handwriting data for verification, and verify the user's logged-in handwriting data based on the distributed handwriting data. Therefore, an efficient and fast handwriting data distribution method is urgently needed. Summary of the invention

[0004] One of the purposes of the present invention is to provide a handwriting data distribution method based on big data, which adopts the method of constructing data distribution nodes, distributing handwriting data in a coordinated manner based on the data distribution nodes, and completing the distribution of handwriting data by data storage nodes, thereby reducing the load of distribution by data storage nodes alone and improving the efficiency of handwriting data distribution by data storage nodes.

[0005] A handwriting data distribution method based on big data provided by an embodiment of the present invention is applied to a data distribution node, comprising:

[0006] Step S1: Obtain handwriting data acquisition request from the client;

[0007] Step S2: parsing the handwriting data acquisition request, and determining information of target data corresponding to the handwriting data acquisition request;

[0008] Step S3: querying a preset storage path library to determine the data storage node corresponding to the target data;

[0009] Step S4: Generate connection information and verification information of the data storage node based on the pre-stored node information of the data storage node;

[0010] Step S5: Send the connection information to the client, and send the verification information to the data storage node; the client connects to the data storage node based on the connection information and the verification information, and after the connection, the data storage node distributes the target data to the client.

[0011] Preferably, before step S1, the client performs the following operations:

[0012] Step S101: obtaining handwriting input by a user through a handwriting input device;

[0013] Step S102: recognizing the handwriting to obtain first recognition information;

[0014] Step S103: Obtain the current interface of the client;

[0015] Step S104A: when the interface is a preset trigger interface corresponding to the handwriting data acquisition request, obtaining a preset request library;

[0016] Step S105: determining data acquisition information based on the request library and the first identification information;

[0017] Step S106: Acquire the status information of the client's connection device and the client's operating status;

[0018] Step S107: Generate current environment information of the client based on the running state and state information;

[0019] Step S108: Generate a handwriting data acquisition request based on the current environment information and data acquisition information.

[0020] Preferably, the client further performs the following operations:

[0021] Step S104B: When the interface is not the preset trigger interface corresponding to the handwriting data acquisition request, the display device of the client is monitored, and when the current interface of the display device switches to the trigger interface within the preset time, steps S105 to S108 are executed.

[0022] Preferably, when the client executes step S101, the following operations are performed:

[0023] When the user touches the input screen of the handwriting input device with a palm and slides from one side to the other side, the handwriting input device is controlled to enter the handwriting collection mode;

[0024] The handwriting input device is sampled based on a preset time interval. When the preset N data sampled continuously are valid, the sampled data are recorded; when the preset M data sampled continuously are invalid, the sampling data recording ends and the recorded sampled data is regarded as a handwriting.

[0025] Preferably, step S2: parsing the handwriting data acquisition request and determining information of target data corresponding to the handwriting data acquisition request includes:

[0026] Step S21: parsing the handwriting data acquisition request to determine the current environment information of the client;

[0027] Step S22: Determine the security level of the client based on the current environment information;

[0028] Step S23: when the security level is greater than the preset security threshold, the handwriting data acquisition request is parsed again to determine the data acquisition information; and information of the target data is determined from the data acquisition information;

[0029] Wherein, step S22: determining the security level of the client based on the current environment information includes:

[0030] Step S41: parse the current environment information, obtain the status information of the client's connection device and the client's operating status;

[0031] Step S42: constructing an environment parameter vector based on the state information and the operating state;

[0032] Step S43: obtaining a preset environmental safety library, in which the safety vectors and safety degrees are associated in a one-to-one correspondence;

[0033] Step S44: Calculate the matching degree between the environment parameter vector and the security vector; when the matching degree is the maximum, the security degree of the security vector is used as the security degree of the current input environment;

[0034] The following formula is used to calculate the matching degree between the environmental parameter vector and the safety vector:

[0035]

[0036] Wherein, P is the matching degree between the environment parameter vector and the security vector; n is the number of data of the environment parameter vector or the number of data of the security vector; a i is the value of the i-th data of the environmental parameter vector; b i is the value of the i-th data of the security vector.

[0037] Preferably, the sampling data includes writing force, and the sampling method of writing force is as follows:

[0038]

[0039] In the formula, F j is the writing force of the hth stroke; I is the total number of stroke sampling points; f θ,h is the strength of the θth sampling point of the hth stroke, f ω,h is the strength of the ωth sampling point of the hth stroke; when the strength of the θth sampling point of the hth stroke falls on The probability is greater than When , O takes the value of 1, otherwise, it takes the value of 0; γ is the preset first correction coefficient.

[0040] Preferably, step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes:

[0041] Get the security level of the client.

[0042] Obtain a comparison table of preset security levels and query permissions;

[0043] Based on the security level and comparison table, determine the client's query permissions for the storage path library;

[0044] Query the storage path library based on query permissions.

[0045] Preferably, the handwriting data distribution method based on big data further includes:

[0046] Step S6: monitor the operation status of the data storage node by running the monitoring node, and when an abnormality occurs, re-provide the data storage node for the client;

[0047] and / or,

[0048] Step S7: predicting the abnormal probability of the data storage node based on the operating status, and re-providing the data storage node to the client when the abnormal probability is greater than a preset threshold;

[0049] Among them, the abnormal probability of the data storage node is predicted based on the operating status, including:

[0050] Obtaining the operating parameters of the data storage node identification operating status;

[0051] The operating parameters are feature extracted, and the extracted feature values ​​are brought into the preset neural network model to obtain the prediction factors. Based on the prediction factors, the preset abnormal probability table is queried to obtain the abnormal probability of the data storage node.

[0052] Preferably, step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes:

[0053] Get the client's first location information,

[0054] Obtain the number of data storage nodes storing target data, and when the number is greater than one, obtain second location information of each data storage node;

[0055] The distance between the first position information and each second position information is calculated, and the data storage node closest to the first position information is selected as the data storage node corresponding to the target data.

[0056] A handwriting data distribution system based on big data of the present invention is applied to a data distribution node, comprising:

[0057] A request acquisition module is used to obtain the handwriting data acquisition request from the client;

[0058] A parsing module, used for parsing a handwriting data acquisition request and determining information of target data corresponding to the handwriting data acquisition request;

[0059] A determination module is used to query a preset storage path library to determine the data storage node corresponding to the target data;

[0060] A generating module, used to generate connection information and verification information of the data storage node based on the node information of the data storage node stored in advance;

[0061] The distribution module is used to send connection information to the client and send verification information to the data storage node; the client connects to the data storage node based on the connection information and verification information, and after the connection, the data storage node distributes the target data to the client.

[0062] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0065] Figure 1 Schematic diagram of a handwriting data distribution method based on big data in an embodiment of the present invention;

[0066] Figure 2 Schematic diagram of another handwriting data distribution method based on big data in an embodiment of the present invention;

[0067] Figure 3 It is a schematic diagram of a handwriting data distribution system based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] The embodiment of the present invention provides a handwriting data distribution method based on big data, such as Figure 1 As shown, it is applied to the data distribution node, including:

[0070] Step S1: Obtain handwriting data acquisition request from the client;

[0071] Step S2: parsing the handwriting data acquisition request, and determining information of target data corresponding to the handwriting data acquisition request;

[0072] Step S3: querying a preset storage path library to determine the data storage node corresponding to the target data;

[0073] Step S4: Generate connection information and verification information of the data storage node based on the pre-stored node information of the data storage node;

[0074] Step S5: Send the connection information to the client, and send the verification information to the data storage node; the client connects to the data storage node based on the connection information and the verification information, and after the connection, the data storage node distributes the target data to the client.

[0075] The working principle and beneficial effects of the above technical solution are:

[0076] A data distribution node is constructed on the big data platform to coordinate the distribution of handwriting data. The data distribution node obtains the handwriting data acquisition request from the client. The client generally issues a handwriting data acquisition request when using handwriting data to verify login, authority verification, etc. The data distribution node parses the handwriting data acquisition request. The acquisition request is mainly to obtain standard handwriting data used to verify the handwriting data of the user login. The handwriting acquisition request is parsed to determine the information of the target data corresponding to the handwriting data acquisition request, mainly the identification of the target data, which is used to find the target data; the data storage node where the target data is located is determined by querying the preset storage path library, and then the connection information and verification information for the data storage node to connect with the client and distribute verification are generated. The client uses the connection information to connect with the data storage node, and the data storage node uses the verification information to verify the connection. When the verification is passed, the data storage node distributes the handwriting data to the client; the security of data distribution is improved by connecting and verifying the connection information and verification information; the data distribution is coordinated by the data distribution node, and the data storage node only needs to complete the data transmission, which improves the efficiency of the data storage node in distributing handwriting data.

[0077] The handwriting data distribution method based on big data of the present invention adopts the method of constructing data distribution nodes, and distributes the handwriting data in a coordinated manner based on the data distribution nodes. The distribution of handwriting data is completed by the data storage nodes, which reduces the load of distribution by the data storage nodes alone and improves the efficiency of handwriting data distribution by the data storage nodes.

[0078] In one embodiment, before step S1, Figure 2 As shown, the client performs the following operations:

[0079] Step S101: obtaining handwriting input by a user through a handwriting input device;

[0080] Step S102: recognizing the handwriting to obtain first recognition information;

[0081] Step S103: Obtain the current interface of the client;

[0082] Step S104A: when the interface is a preset trigger interface corresponding to the handwriting data acquisition request, obtaining a preset request library;

[0083] Step S105: determining data acquisition information based on the request library and the first identification information;

[0084] Step S106: Acquire the status information of the client's connection device and the client's operating status;

[0085] Step S107: Generate current environment information of the client based on the running state and state information;

[0086] Step S108: Generate a handwriting data acquisition request based on the current environment information and data acquisition information.

[0087] The working principle and beneficial effects of the above technical solution are:

[0088] Generally speaking, the client can input handwriting through the handwriting input device only when the handwriting data acquisition request interface is the current interface, and then generate a handwriting data acquisition request based on the handwriting, operating status, and connection status, providing basic data for overall judgment for the data distribution node, mastering the overall security of the client, and improving the security of data distribution.

[0089] In one embodiment, the client further performs the following operations:

[0090] Step S104B: When the interface is not the preset trigger interface corresponding to the handwriting data acquisition request, the display device of the client is monitored, and when the current interface of the display device switches to the trigger interface within the preset time, steps S105 to S108 are executed.

[0091] The working principle and beneficial effects of the above technical solution are:

[0092] Usually, users first open the trigger interface and then input handwriting through the handwriting input device, and then the client automatically generates a corresponding request to obtain the verification handwriting data based on the input handwriting. However, it is also possible that the user forgets to open the trigger interface and inputs the handwriting through the handwriting input device first. In this way, when the user opens the trigger interface again, he needs to input the handwriting again. Through the scheme of this embodiment, when the current interface when the user inputs handwriting through the handwriting input device is not the trigger interface, by monitoring the current interface, when it switches to the trigger interface within a preset time, the input handwriting and the current interface are directly used to generate a handwriting data acquisition request, thereby avoiding the user from inputting handwriting again, thereby improving work efficiency and the intelligence of the handwriting data acquisition trigger.

[0093] In one embodiment, when the client executes step S101, the following operations are performed:

[0094] When the user touches the input screen of the handwriting input device with a palm and slides from one side to the other side, the handwriting input device is controlled to enter the handwriting collection mode;

[0095] The handwriting input device is sampled based on a preset time interval. When the preset N data sampled continuously are valid, the sampled data are recorded; when the preset M data sampled continuously are invalid, the sampling data recording ends and the recorded sampled data is regarded as a handwriting.

[0096] The working principle and beneficial effects of the above technical solution are:

[0097] By touching with the palm and sliding from one side to the other, the handwriting input device is activated on the one hand, and on the other hand, it can be ensured that there is no foreign matter on the input screen of the handwriting input device, which will not affect the accuracy of this handwriting collection. The sampling start point of handwriting is taken as the preset N data as valid, and the end point of handwriting sampling is taken as the invalid data, which ensures the accuracy of handwriting sampling. N is at least 2, and M is at least 2.

[0098] In one embodiment, the handwriting input by the user through the handwriting input device is numbered, and the handwriting is associated in sequence according to the order in which the user opens the trigger interface on the client, so as to realize the generation of batch handwriting data acquisition requests, thereby improving work efficiency. For example, when the client builds a local verification library, the users of the local verification library are queued up to input handwriting on the handwriting input device in sequence, and then the client synchronously opens the trigger interface, thereby improving the efficiency of building the local verification library.

[0099] In one embodiment, step S2: parsing the handwriting data acquisition request and determining information of target data corresponding to the handwriting data acquisition request includes:

[0100] Step S21: parsing the handwriting data acquisition request to determine the current environment information of the client;

[0101] Step S22: Determine the security level of the client based on the current environment information;

[0102] Step S23: when the security level is greater than the preset security threshold, the handwriting data acquisition request is parsed again to determine the data acquisition information; and information of the target data is determined from the data acquisition information;

[0103] Wherein, step S22: determining the security level of the client based on the current environment information includes:

[0104] Step S41: parse the current environment information, obtain the status information of the client's connection device and the client's operating status;

[0105] Step S42: constructing an environment parameter vector based on the state information and the operating state;

[0106] Step S43: obtaining a preset environmental safety library, in which the safety vectors and safety degrees are associated in a one-to-one correspondence;

[0107] Step S44: Calculate the matching degree between the environment parameter vector and the security vector; when the matching degree is the maximum, the security degree of the security vector is used as the security degree of the current input environment;

[0108] The following formula is used to calculate the matching degree between the environmental parameter vector and the safety vector:

[0109]

[0110] Wherein, P is the matching degree between the environment parameter vector and the security vector; n is the number of data of the environment parameter vector or the number of data of the security vector; a i is the value of the i-th data of the environmental parameter vector; b i is the value of the i-th data of the security vector.

[0111] The working principle and beneficial effects of the above technical solution are:

[0112] By analyzing the current environment information of the handwriting data acquisition request, it is determined whether the client is safe, thereby ensuring the safety of the handwriting data. The current environment information is considered from two aspects: the first is whether the device connected to the client is safe, which is analyzed based on the status information of the connected device; the second is whether the client's status is safe, which is analyzed based on the client's running status. The client's running status includes: running programs, and whether each program is on the safe list.

[0113] In one embodiment, the sampled data includes writing force, and the sampling method of writing force is as follows:

[0114]

[0115] In the formula, F h is the writing force of the hth stroke; I is the total number of stroke sampling points; f θ,h is the strength of the θth sampling point of the hth stroke, f ω,h is the strength of the ωth sampling point of the hth stroke; when the strength of the θth sampling point of the hth stroke falls on The probability is greater than When , O takes the value of 1, otherwise, it takes the value of 0; γ is the preset first correction coefficient.

[0116] The working principle and beneficial effects of the above technical solution are:

[0117] In determining the writing force, the force is corrected using the deviation of each sampling point, thereby improving the accuracy of the force determination.

[0118] In one embodiment, step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes:

[0119] Get the security level of the client.

[0120] Obtain a comparison table of preset security levels and query permissions;

[0121] Based on the security level and comparison table, determine the client's query permissions for the storage path library;

[0122] Query the storage path library based on query permissions.

[0123] The working principle and beneficial effects of the above technical solution are:

[0124] By associating security with query permissions, the security of handwriting data distribution is improved. The determination of security in this application is based on the status information of the device connected to the client to a certain extent. Therefore, the security represents the sampling mode of the handwriting input device to a certain extent. The paths of different sampling mode data storage in the storage path library are queried based on different sampling modes; different sampling mode data in the storage path library are queried with different permissions.

[0125] In one embodiment, the handwriting data distribution method based on big data further includes:

[0126] Step S6: monitor the operation status of the data storage node by running the monitoring node, and when an abnormality occurs, re-provide the data storage node for the client;

[0127] and / or,

[0128] Step S7: predicting the abnormal probability of the data storage node based on the operating status, and re-providing the data storage node to the client when the abnormal probability is greater than a preset threshold;

[0129] Among them, the abnormal probability of the data storage node is predicted based on the operating status, including:

[0130] Obtaining the operating parameters of the data storage node identification operating status;

[0131] The operating parameters are feature extracted, and the extracted feature values ​​are brought into the preset neural network model to obtain the prediction factors. Based on the prediction factors, the preset abnormal probability table is queried to obtain the abnormal probability of the data storage node.

[0132] The working principle and beneficial effects of the above technical solution are:

[0133] The data storage nodes that execute distribution are monitored and predicted. When an abnormality occurs or there are signs that the distribution execution is endangered (the probability of abnormality is greater than the preset threshold), the data storage node is re-provided to the client to ensure that the client can receive the desired handwriting data and ensure the effective distribution of handwriting data.

[0134] In one embodiment, step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes:

[0135] Get the client's first location information,

[0136] Obtain the number of data storage nodes storing target data, and when the number is greater than one, obtain second location information of each data storage node;

[0137] The distance between the first position information and each second position information is calculated, and the data storage node closest to the first position information is selected as the data storage node corresponding to the target data.

[0138] The working principle and beneficial effects of the above technical solution are:

[0139] By determining the location, the transmission time of distributed data can be shortened and the efficiency of distribution can be improved.

[0140] The present invention is a handwriting data distribution system based on big data, which is applied to data distribution nodes, such as Figure 3 As shown, including:

[0141] The request acquisition module 11 is used to acquire the handwriting data acquisition request from the client;

[0142] The parsing module 12 is used to parse the handwriting data acquisition request and determine the information of the target data corresponding to the handwriting data acquisition request;

[0143] A determination module 13 is used to query a preset storage path library to determine a data storage node corresponding to the target data;

[0144] A generating module 14, configured to generate connection information and verification information of a data storage node based on node information of the pre-stored data storage node;

[0145] The distribution module 15 is used to send the connection information to the client and the verification information to the data storage node; the client connects to the data storage node based on the connection information and the verification information, and after the connection, the data storage node distributes the target data to the client.

[0146] The working principle and beneficial effects of the above technical solution are:

[0147] A data distribution node is constructed on the big data platform to coordinate the distribution of handwriting data. The data distribution node obtains the handwriting data acquisition request from the client. The client generally issues a handwriting data acquisition request when using handwriting data to verify login, authority verification, etc. The data distribution node parses the handwriting data acquisition request. The acquisition request is mainly to obtain standard handwriting data used to verify the handwriting data of the user login. The handwriting acquisition request is parsed to determine the information of the target data corresponding to the handwriting data acquisition request, mainly the identification of the target data, which is used to find the target data; the data storage node where the target data is located is determined by querying the preset storage path library, and then the connection information and verification information for the data storage node to connect with the client and distribute verification are generated. The client uses the connection information to connect with the data storage node, and the data storage node uses the verification information to verify the connection. When the verification is passed, the data storage node distributes the handwriting data to the client; the security of data distribution is improved by connecting and verifying the connection information and verification information; the data distribution is coordinated by the data distribution node, and the data storage node only needs to complete the data transmission, which improves the efficiency of the data storage node in distributing handwriting data.

[0148] The handwriting data distribution system based on big data of the present invention adopts the method of constructing data distribution nodes, distributing handwriting data in a coordinated manner based on the data distribution nodes, and the distribution of handwriting data is completed by the data storage nodes, which reduces the load of distribution by the data storage nodes alone and improves the efficiency of handwriting data distribution by the data storage nodes.

[0149] In one embodiment, the client performs the following operations:

[0150] Acquire handwriting input by the user through a handwriting input device;

[0151] Recognize the handwriting to obtain first recognition information;

[0152] Get the current interface of the client;

[0153] When the interface is a preset trigger interface corresponding to the handwriting data acquisition request, obtaining a preset request library;

[0154] Determining data acquisition information based on the request library and the first identification information;

[0155] Obtain the status information of the client's connected device and the client's operating status;

[0156] Generate the client's current environment information based on the running status and status information;

[0157] Based on the current environment information and the data acquisition information, a handwriting data acquisition request is generated.

[0158] The working principle and beneficial effects of the above technical solution are:

[0159] Generally speaking, the client can input handwriting through the handwriting input device only when the handwriting data acquisition request interface is the current interface, and then generate a handwriting data acquisition request based on the handwriting, operating status, and connection status, providing basic data for overall judgment for the data distribution node, mastering the overall security of the client, and improving the security of data distribution.

[0160] In one embodiment, the client further performs the following operations:

[0161] When the interface is not the preset trigger interface corresponding to the handwriting data acquisition request, the display device of the client is monitored, and when the current interface of the display device switches to the trigger interface within a preset time, the handwriting data acquisition request generation operation is executed.

[0162] The working principle and beneficial effects of the above technical solution are:

[0163] Usually, users first open the trigger interface and then input handwriting through the handwriting input device, and then the client automatically generates a corresponding request to obtain the verification handwriting data based on the input handwriting. However, it is also possible that the user forgets to open the trigger interface and inputs the handwriting through the handwriting input device first. In this way, when the user opens the trigger interface again, he needs to input the handwriting again. Through the scheme of this embodiment, when the current interface when the user inputs handwriting through the handwriting input device is not the trigger interface, by monitoring the current interface, when it switches to the trigger interface within a preset time, the input handwriting and the current interface are directly used to generate a handwriting data acquisition request, thereby avoiding the user from inputting handwriting again, thereby improving work efficiency and the intelligence of the handwriting data acquisition trigger.

[0164] In one embodiment, when the client obtains the handwriting input by the user through the handwriting input device, the following operations are performed:

[0165] When the user touches the input screen of the handwriting input device with a palm and slides from one side to the other side, the handwriting input device is controlled to enter the handwriting collection mode;

[0166] The handwriting input device is sampled based on a preset time interval. When the preset N data sampled continuously are valid, the sampled data are recorded; when the preset M data sampled continuously are invalid, the sampling data recording ends and the recorded sampled data is regarded as a handwriting.

[0167] The working principle and beneficial effects of the above technical solution are:

[0168] By touching with the palm and sliding from one side to the other, the handwriting input device is activated on the one hand, and on the other hand, it can be ensured that there is no foreign matter on the input screen of the handwriting input device, which will not affect the accuracy of this handwriting collection. The sampling start point of handwriting is taken as the preset N data as valid, and the end point of handwriting sampling is taken as the invalid data, which ensures the accuracy of handwriting sampling. N is at least 2, and M is at least 2.

[0169] In one embodiment, the parsing module performs the following operations:

[0170] Parse the handwriting data acquisition request and determine the current environment information of the client;

[0171] Determine the security level of the client based on the current environment information;

[0172] When the security level is greater than a preset security threshold, the handwriting data acquisition request is parsed again to determine data acquisition information; and information of the target data is determined from the data acquisition information;

[0173] The security level of the client is determined based on the current environment information, including:

[0174] Analyze the current environment information, obtain the status information of the client's connected devices and the client's operating status;

[0175] Constructing an environmental parameter vector based on the state information and the running state;

[0176] Obtain a preset environmental safety library, in which the safety vectors and safety degrees are associated one-to-one;

[0177] Calculate the matching degree between the environment parameter vector and the security vector; when the matching degree is the largest, the security degree of the security vector is used as the security degree of the current input environment;

[0178] The following formula is used to calculate the matching degree between the environmental parameter vector and the safety vector:

[0179]

[0180] Wherein, P is the matching degree between the environment parameter vector and the security vector; n is the number of data of the environment parameter vector or the number of data of the security vector; a i is the value of the i-th data of the environmental parameter vector; b i is the value of the i-th data of the security vector.

[0181] The working principle and beneficial effects of the above technical solution are:

[0182] By analyzing the current environment information of the handwriting data acquisition request, it is determined whether the client is safe, thereby ensuring the safety of the handwriting data. The current environment information is considered from two aspects: the first is whether the device connected to the client is safe, which is analyzed based on the status information of the connected device; the second is whether the client's status is safe, which is analyzed based on the client's running status. The client's running status includes: running programs, and whether each program is on the safe list.

[0183] In one embodiment, the sampled data includes writing force, and the sampling method of writing force is as follows:

[0184]

[0185] In the formula, F h is the writing force of the hth stroke; I is the total number of stroke sampling points; f θ,h is the strength of the θth sampling point of the hth stroke, f ω,h is the strength of the ωth sampling point of the hth stroke; when the strength of the θth sampling point of the hth stroke falls on The probability is greater than When , O takes the value of 1, otherwise, it takes the value of 0; γ is the preset first correction coefficient.

[0186] The working principle and beneficial effects of the above technical solution are:

[0187] In determining the writing force, the force is corrected using the deviation of each sampling point, thereby improving the accuracy of the force determination.

[0188] In one embodiment, the determination module performs the following operations:

[0189] Get the security level of the client.

[0190] Obtain a comparison table of preset security levels and query permissions;

[0191] Based on the security level and comparison table, determine the client's query permissions for the storage path library;

[0192] Query the storage path library based on query permissions.

[0193] The working principle and beneficial effects of the above technical solution are:

[0194] By associating security with query permissions, the security of handwriting data distribution is improved. The determination of security in this application is based on the status information of the device connected to the client to a certain extent. Therefore, the security represents the sampling mode of the handwriting input device to a certain extent. The paths of different sampling mode data storage in the storage path library are queried based on different sampling modes; different sampling mode data in the storage path library are queried with different permissions.

[0195] In one embodiment, the handwriting data distribution system based on big data further includes:

[0196] The monitoring module is used to monitor the operation status of the data storage node by running the monitoring node, and when an abnormality occurs, re-provide the data storage node to the client;

[0197] and / or,

[0198] The monitoring module is also used to predict the abnormal probability of the data storage node based on the operating status, and when the abnormal probability is greater than a preset threshold, the data storage node is re-provided to the client;

[0199] Among them, the abnormal probability of the data storage node is predicted based on the operating status, including:

[0200] Obtaining the operating parameters of the data storage node identification operating status;

[0201] The operating parameters are feature extracted, and the extracted feature values ​​are brought into the preset neural network model to obtain the prediction factors. Based on the prediction factors, the preset abnormal probability table is queried to obtain the abnormal probability of the data storage node.

[0202] The working principle and beneficial effects of the above technical solution are:

[0203] The data storage nodes that execute distribution are monitored and predicted. When an abnormality occurs or there are signs that the distribution execution is endangered (the probability of abnormality is greater than the preset threshold), the data storage node is re-provided to the client to ensure that the client can receive the desired handwriting data and ensure the effective distribution of handwriting data.

[0204] In one embodiment, the determination module further performs the following operations:

[0205] Get the client's first location information,

[0206] Obtain the number of data storage nodes storing target data, and when the number is greater than one, obtain second location information of each data storage node;

[0207] The distance between the first position information and each second position information is calculated, and the data storage node closest to the first position information is selected as the data storage node corresponding to the target data.

[0208] The working principle and beneficial effects of the above technical solution are:

[0209] By determining the location, the transmission time of distributed data can be shortened and the efficiency of distribution can be improved.

[0210] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A handwriting data distribution method based on big data, applied to a data distribution node, characterized in that: include: Step S1: Obtain handwriting data acquisition request from the client; Step S2: parsing the handwriting data acquisition request to determine information of target data corresponding to the handwriting data acquisition request; Step S3: querying a preset storage path library to determine the data storage node corresponding to the target data; Step S4: generating connection information and verification information of the data storage node based on the pre-stored node information of the data storage node; Step S5: sending the connection information to the client, and sending the verification information to the data storage node; the client connects to the data storage node based on the connection information and the verification information, and after the connection, the data storage node distributes the target data to the client; The step S2: parsing the handwriting data acquisition request and determining information of target data corresponding to the handwriting data acquisition request includes: Step S21: parsing the handwriting data acquisition request to determine the current environment information of the client; Step S22: Determine the security level of the client based on the current environment information; Step S23: when the security level is greater than a preset security threshold, re-analyze the handwriting data acquisition request to determine data acquisition information; and determine information of the target data from the data acquisition information; Wherein, the step S22: determining the security level of the client based on the current environment information includes: Step S41: parsing the current environment information, obtaining the status information of the connection device of the client and the operation status of the client; Step S42: constructing an environment parameter vector based on the state information and the operating state; Step S43: obtaining a preset environmental safety library, in which safety vectors and safety degrees are associated in a one-to-one correspondence; Step S44: calculating the matching degree between the environment parameter vector and the security vector; obtaining the security degree of the security vector when the matching degree is the maximum as the security degree of the current input environment; The following formula is used to calculate the matching degree between the environmental parameter vector and the security vector: Wherein, P is the matching degree between the environment parameter vector and the security vector; n is the number of data of the environment parameter vector or the number of data of the security vector; a i is the value of the i-th data of the environmental parameter vector; b i is the value of the i-th data of the security vector.

2. The handwriting data distribution method based on big data as claimed in claim 1, characterized in that: Before step S1, the client performs the following operations: Step S101: obtaining handwriting input by a user through a handwriting input device; Step S102: recognizing the handwriting to obtain first recognition information; Step S103: obtaining the current interface of the client; Step S104A: when the interface is a preset trigger interface corresponding to the handwriting data acquisition request, obtaining a preset request library; Step S105: determining data acquisition information based on the request library and the first identification information; Step S106: Acquire the status information of the connection device of the client and the operation status of the client; Step S107: generating current environment information of the client based on the running state and the state information; Step S108: Generate the handwriting data acquisition request based on the current environment information and the data acquisition information.

3. The handwriting data distribution method based on big data as claimed in claim 2, characterized in that: The client also performs the following operations: Step S104B: When the interface is not the preset trigger interface corresponding to the handwriting data acquisition request, the display device of the client is monitored, and when the current interface of the display device switches to the trigger interface within a preset time, steps S105 to S108 are executed.

4. The handwriting data distribution method based on big data as claimed in claim 2, characterized in that: When the client executes step S101, the following operations are performed: When the user touches the input screen of the handwriting input device with a palm and slides from one side to the other side, the handwriting input device is controlled to enter the handwriting collection mode; Sampling data from the handwriting input device based on a preset time interval, and recording the sampled data when N preset data sampled continuously are valid; When the preset M data of continuous sampling are invalid, the recording of the sampling data is terminated, and the recorded sampling data is regarded as a handwriting.

5. The handwriting data distribution method based on big data as claimed in claim 4, characterized in that: The sampling data includes writing force, and the sampling method of the writing force is as follows: In the formula, F h is the writing force of the h-th stroke; I is the total number of stroke sampling points; f θ,h is the strength of the θth sampling point of the hth stroke, f ω,h is the strength of the ωth sampling point of the hth stroke; when the strength of the θth sampling point of the hth stroke falls on The probability is greater than When , O takes the value of 1, otherwise, it takes the value of 0; γ is the preset first correction coefficient.

6. The handwriting data distribution method based on big data as claimed in claim 4, characterized in that: The step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes: Obtain the security level of the client, Obtain a comparison table of preset security levels and query permissions; Based on the security level and the comparison table, determining the query authority of the client for the storage path library; The storage path library is queried based on the query authority.

7. The handwriting data distribution method based on big data as claimed in claim 1, characterized in that: Also includes: Step S6: monitoring the operation status of the data storage node by running the monitoring node, and re-providing the data storage node for the client when an abnormality occurs; and / or, Step S7: predicting the abnormal probability of the data storage node based on the operating state, and re-providing the data storage node for the client when the abnormal probability is greater than a preset threshold; The step of predicting the abnormal probability of the data storage node based on the operating state includes: Obtaining the operating parameters of the data storage node identifying the operating state; Feature extraction is performed on the operating parameters, the extracted feature values ​​are brought into a preset neural network model to obtain prediction factors, and a preset abnormal probability table is queried based on the prediction factors to obtain the abnormal probability of the data storage node.

8. The handwriting data distribution method based on big data as claimed in claim 1, characterized in that: The step S3: querying a preset storage path library to determine the data storage node corresponding to the target data includes: obtaining first location information of the client, Acquire the number of data storage nodes storing the target data, and when the number is greater than one, acquire second location information of each of the data storage nodes; The distance between the first position information and each of the second position information is calculated, and the data storage node with the shortest distance is selected as the data storage node corresponding to the target data.

9. A handwriting data distribution system based on big data, applied to data distribution nodes, characterized in that: include: A request acquisition module is used to obtain the handwriting data acquisition request from the client; A parsing module, used for parsing the handwriting data acquisition request and determining information of target data corresponding to the handwriting data acquisition request; A determination module, used to query a preset storage path library to determine the data storage node corresponding to the target data; A generating module, configured to generate connection information and verification information of the data storage node based on pre-stored node information of the data storage node; A distribution module, used to send the connection information to the client, and send the verification information to the data storage node; the client connects to the data storage node based on the connection information and the verification information, and after the connection, the data storage node distributes the target data to the client; The parsing module is used to parse the handwriting data acquisition request and determine the information of the target data corresponding to the handwriting data acquisition request, including: Step S21: parsing the handwriting data acquisition request to determine the current environment information of the client; Step S22: Determine the security level of the client based on the current environment information; Step S23: when the security level is greater than a preset security threshold, re-analyze the handwriting data acquisition request to determine data acquisition information; and determine information of the target data from the data acquisition information; Wherein, the step S22: determining the security level of the client based on the current environment information includes: Step S41: parsing the current environment information, obtaining the status information of the connection device of the client and the operation status of the client; Step S42: constructing an environment parameter vector based on the state information and the operating state; Step S43: obtaining a preset environmental safety library, in which safety vectors and safety degrees are associated in a one-to-one correspondence; Step S44: calculating the matching degree between the environment parameter vector and the security vector; obtaining the security degree of the security vector when the matching degree is the maximum as the security degree of the current input environment; The following formula is used to calculate the matching degree between the environmental parameter vector and the security vector: Among them, P is the matching degree between the environmental parameter vector and the security vector; n is the number of data of the environmental parameter vector or the number of data of the security vector; ai is the value of the i-th data of the environmental parameter vector; bi is the value of the i-th data of the security vector.

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