CN110688678A

CN110688678A - Data processing method, device and equipment applied to block chain

Info

Publication number: CN110688678A
Application number: CN201910926944.4A
Authority: CN
Inventors: 黄凯明; 蔡鸿博; 曾晓东; 林锋; 杨磊
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2019-09-27
Filing date: 2019-09-27
Publication date: 2020-01-14
Anticipated expiration: 2039-09-27
Also published as: CN110688678B; CN113672996A; CN113672996B

Abstract

The embodiment of the specification discloses a data processing method, a device and equipment applied to a block chain, wherein the method comprises the following steps: acquiring target data detected by a target user in a process of executing preset work, wherein the target data comprises one or more items of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work; according to a preset detection rule and the target data, authenticity detection is carried out on the process of executing preset work by the target user so as to determine whether the process of executing the preset work by the target user is real or not; and if the process that the target user executes the preset work is determined to be real, storing the data of the preset work executed by the target user in the target data to a preset block chain node.

Description

Data processing method, device and equipment applied to block chain

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a data processing method, apparatus, and device for a block chain.

Background

The block chain technology has been applied well in the direction of on-line data tamper resistance, anti-counterfeiting and traceability, if data is added into a block chain, the data cannot be changed and repudiated, obviously, if the data lacks the authenticity judgment link before being added into the block chain, the value of the data added into the block chain is questioned, and how to ensure that the data is authentic and credible before being added into the block chain becomes an important problem to be solved at present.

At present, there are many repetitive critical operations, such as fire-fighting inspection, road safety inspection, etc., the above operations usually lack an effective monitoring mechanism, and in addition, the quality of the working personnel is uneven, so that the conditions of missing inspection or forging work records (or work data) are easily generated in the process of executing the above operations, once the above conditions occur, it is likely to generate a catastrophic result which is difficult to estimate, and the above conditions can be usually avoided by adopting a workload detection mode based on positioning and a detection mode by means of hardware equipment pre-embedded in a specified place. However, because the workload detection method based on positioning depends on the positioning accuracy, it is difficult to determine whether the worker really arrives at the designated location, and for the detection method by using the hardware device pre-embedded in the designated location, the detection device and the network need to be laid in the designated location, which results in higher hardware cost and poorer flexibility. Therefore, it is necessary to provide a working authenticity detection mechanism with higher flexibility without depending on positioning accuracy.

Disclosure of Invention

An object of the embodiments of the present disclosure is to provide a data processing method, apparatus, and device applied to a block chain, so as to provide a working authenticity detection mechanism that does not depend on positioning accuracy and has higher flexibility.

In order to implement the above technical solution, the embodiments of the present specification are implemented as follows:

the data processing method applied to the block chain provided by the embodiment of the specification comprises the following steps:

acquiring target data detected by a target user in a process of executing preset work, wherein the target data comprises one or more items of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work;

according to a preset detection rule and the target data, authenticity detection is carried out on the process of executing preset work by the target user so as to determine whether the process of executing the preset work by the target user is real or not;

and if the process that the target user executes the preset work is determined to be real, storing the data of the preset work executed by the target user in the target data to a preset block chain node.

Optionally, the method further comprises:

acquiring data detected by a user in a process of executing a preset work, and taking the acquired data as sample data;

dividing the process of the user executing the predetermined work into one or more events under different scenes based on the sample data;

performing statistical analysis on the sample data aiming at one or more events under different scenes, and determining the data distribution and change condition of the sample data in the process of executing preset work by a user;

and determining a detection rule corresponding to the process of executing the preset work by the user based on the data distribution and the change condition of the sample data in the process of executing the preset work by the user.

Optionally, the dividing, based on the sample data, a process of the user performing a predetermined job into one or more events in different scenarios includes:

acquiring recorded video data of the user in a preset working process;

and dividing the process of executing the scheduled work by the user into one or more events under different scenes based on the sample data and the recorded video data.

Optionally, the data of the target user performing the predetermined work includes events in the one or more different scenarios and execution times of the events in the one or more different scenarios, and the method further includes:

storing the environmental data, the behavior data of the target user and the network connection information in the target data to the predetermined tile link point.

Optionally, the detection rule includes one or more of a range of the number of events, a time range of duration of each event, a variation range of the environmental data, a range of the number of behaviors corresponding to the behavior data of the target user, and a range of the number of network connections corresponding to the network connection information.

Optionally, the events in the plurality of different scenarios include a login event and an event for detecting different predetermined detection points to which the user performs a predetermined job.

Optionally, the predetermined detection point is provided with a graphic code or a near field communication tag, and detecting the predetermined detection point includes scanning the graphic code or detecting the near field communication tag based on near field communication.

Optionally, the target data comprises one or more of wirelessly connected data, position data, barometric pressure data, acceleration data, altitude data, time and angular velocity data.

The data processing device applied to the block chain provided by the embodiment of the specification comprises:

the data acquisition module is used for acquiring target data detected by a target user in a process of executing a preset work, wherein the target data comprises one or more items of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work;

the authenticity judgment module is used for carrying out authenticity detection on the process of the target user executing the preset work according to a preset detection rule and the target data so as to determine whether the process of the target user executing the preset work is real or not;

and the storage module is used for storing the data of the target user executing the preset work in the target data to a preset block chain node if the process that the target user executes the preset work is determined to be real.

Optionally, the apparatus further comprises:

the system comprises a sample data acquisition module, a data acquisition module and a data processing module, wherein the sample data acquisition module is used for acquiring data detected by a user in a preset working process and taking the acquired data as sample data;

the event dividing module is used for dividing the process of the user executing the preset work into one or more events under different scenes based on the sample data;

the analysis module is used for carrying out statistical analysis on the sample data aiming at one or more events under different scenes and determining the data distribution and the change condition of the sample data in the process of executing the preset work by a user;

and the rule determining module is used for determining a detection rule corresponding to the process of executing the preset work by the user based on the data distribution and the change condition of the sample data in the process of executing the preset work by the user.

Optionally, the event partitioning module includes:

the video data acquisition unit is used for acquiring the recorded video data of the user in the process of executing the scheduled work;

and the event dividing unit is used for dividing the process of executing the scheduled work by the user into one or more events under different scenes based on the sample data and the recorded video data.

Optionally, the data of the target user performing the predetermined work includes events in the one or more different scenarios and execution times of the events in the one or more different scenarios, and the apparatus further includes:

and the data storage module is used for storing the environmental data in the target data, the behavior data of the target user and the network connection information to the preset block link point.

An embodiment of the present specification provides a data processing apparatus applied to a block chain, where the data processing apparatus applied to the block chain includes:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

As can be seen from the above technical solutions provided by the embodiments of the present specification, in the embodiments of the present specification, target data detected by a target user in a process of performing a predetermined job is obtained, where the target data includes one or more of environment data of the target user in the process of performing the predetermined job, behavior data of the target user, and network connection information of the target user in the process of performing the predetermined job, and then, according to a predetermined detection rule and the target data, authenticity detection is performed on the process of performing the predetermined job by the target user to determine whether the process of performing the predetermined job by the target user is authentic, and if it is determined that the process of performing the predetermined job by the target user is authentic, data of performing the predetermined job by the target user in the target data is stored in a predetermined block link point, so that, the environment data detected by the target user in the process of performing the predetermined job, The behavior data and the network connection information of the target user are matched with a preset detection rule to judge the authenticity of the process of executing the preset work by the target user, and under the real condition, the data of executing the preset work by the target user in the target data is stored in a preset block chain node, so that whether the preset work is executed by the target user can be judged based on the detected data, the high positioning precision is not needed, and the flexibility of work authenticity detection is high.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a block diagram illustrating an embodiment of a data processing method applied to a blockchain;

FIG. 2 is a block diagram illustrating another embodiment of a data processing method applied to a blockchain;

FIG. 3 is a block diagram illustrating another embodiment of a data processing method applied to a blockchain;

FIG. 4 is a block diagram illustrating another embodiment of a data processing method applied to a blockchain;

FIG. 5 is a schematic view of a fire inspection tour of the present specification;

FIG. 6 is a block diagram illustrating an embodiment of a data processing apparatus for use in a blockchain;

fig. 7 is an embodiment of a data processing apparatus applied to a blockchain according to the present disclosure.

Detailed Description

The embodiment of the specification provides a data processing method, a data processing device and data processing equipment applied to a block chain.

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in this description shall fall within the scope of protection of this document.

Example one

As shown in fig. 1, an embodiment of the present specification provides a data processing method applied to a blockchain, where an execution subject of the method may be a terminal device or a server, where the terminal device may be a mobile terminal device such as a mobile phone or a tablet computer, and may also be a device such as a personal computer. The server may be an independent server or a server cluster including a plurality of servers. The method can be used in processes such as determining the authenticity of certain data before storing the data in the blockchain. In order to improve processing efficiency, the execution in the embodiments of the present specification is described by taking a server as an example, and for the case where the execution subject is a terminal device, the execution may be performed based on the server, which is not described herein again. The method may specifically comprise the steps of:

in step S102, target data detected by the target user during the execution of the predetermined work is obtained, where the target data includes one or more of environment data of the target user during the execution of the predetermined work, behavior data of the target user, and network connection information of the target user during the execution of the predetermined work.

The target user may be any user, and in this embodiment, the target user may be a user who performs a predetermined job. The predetermined work may be any preset work, the predetermined work may include various different operation contents, and the execution of the predetermined work is a process of sequentially completing specified operations in a certain order and achieving a predetermined purpose or target, for example, the predetermined work is used to inspect fire-fighting equipment on each floor in a certain residential building, so that an inspector needs to sequentially arrive at each floor and detect the fire-fighting equipment, and the predetermined work may be, for example, road safety inspection on a road section where certain accidents are frequent, and the like. The target data may be related data of the target user in the process of executing the predetermined work, may include all data of the target user in the process of executing the predetermined work, may also be valid data of the target user in the process of executing the predetermined work, and the like, and the target data may include not only environmental data of the target user in the process of executing the predetermined work, behavior data of the target user, and network connection information of the target user in the process of executing the predetermined work, but also, for example, detected times when the target user executes different behaviors, and other related information recorded by the target user in the process of executing the predetermined work. The environmental data may include barometric pressure, altitude, etc. data for a location or area at which the target user is located during performance of the predetermined task. The behavior data of the target user may include acceleration data, angular velocity data, operation data of the target user, and the like of the target user. The network connection information may include information on Wireless network connection and information on wired network connection, where the information on Wireless network connection includes information on bluetooth connection, information on WiFi (Wireless Fidelity) connection, and the like.

In the implementation, the block chain technology has been applied well in the online data tamper-proofing, anti-counterfeiting and traceability directions, and it is known that once data is added to a block chain, the data cannot be changed and repudiated, and obviously, if the data lacks a authenticity determination link before being added to the block chain, the value of the data added to the block chain is questioned, and how to ensure that the data is authentic before being added to the block chain becomes an important problem to be solved at present.

At present, there are many repetitive critical operations, such as the above-mentioned works of fire-fighting inspection, road safety inspection, etc., the above-mentioned works usually lack an effective monitoring mechanism, and in addition, the quality of the staff is uneven, the condition of missing inspection or forging work records (or work data) is easy to occur in the process of executing the above-mentioned works, and once the above-mentioned condition occurs, it is likely to generate a catastrophic consequence that is difficult to estimate, for example, the condition of missing inspection or forging work records occurs in the security inspection and inspection work of the fire-fighting equipment, when a fire occurs, the fire-fighting equipment just fails and cannot be used, so that the fire is not controlled in time and a serious fire is caused.

In order to solve the problems, the following two modes can be generally adopted, one mode is a workload detection mode based on positioning, taking the fire inspection as an example, an inspection person carries a positioning device in the inspection process, the position change of the inspection person can be recorded through the positioning device, and whether the inspection person arrives at a specified place for inspection is judged through the position change of the inspection person; another mode is to detect by means of hardware equipment pre-buried in a designated place, taking the fire inspection as an example, an inspection switch or an induction device and the like can be arranged at each designated place (such as the position where fire-fighting equipment is placed in each floor) to be inspected, when an inspection person arrives at the designated place, the inspection switch or the induction device can be triggered, and at the moment, the condition that the inspection person arrives at the designated place to perform inspection can be recorded.

However, although the predetermined work can be effectively monitored by the above method, there are many problems that the positioning-based workload detection method depends on the accuracy of positioning, in many working scenarios, the intervals of different designated places may be close and may occur in the same building, and it is difficult to determine whether the worker actually arrives at the designated place due to the positioning error. For the detection mode by means of the hardware equipment pre-buried in the designated place, the detection equipment and the network need to be laid in the designated place, so that the hardware cost is high, and a matched software system is needed for supporting, so that the flexibility of the mode is poor. Therefore, the embodiments of the present disclosure provide a working authenticity detection mechanism with higher flexibility and without depending on positioning accuracy, which may specifically include the following:

for better detection of the process of executing the predetermined work, a detection device may be preset, the detection device may be a handheld or movable electronic device, the detection device may include various sensors, such as a gyroscope, an acceleration sensor, a barometer, a compass, and the like, and the detection device may further include a display screen, a camera, bluetooth, a Near Field Communication (NFC) component, a wireless Communication component, and the like. When a worker (i.e., a target user) needs to perform a predetermined job, the worker may log in the detection device through an account, at this time, the detection device may start a sensor of the detection device and a corresponding component, the target user may carry the detection device to perform the predetermined job, and in the process of performing the predetermined job, the sensor of the target user and the corresponding component acquire corresponding data, which may include, for example, environmental data (e.g., air pressure data, height data, etc.) at a position where the target user is located or around the target user, behavior data (e.g., data related to scanning or detecting an object, etc.) of the target user, network connection information of the target user in the process of performing the predetermined job, and the like.

In addition, in order to save hardware development costs of the detection device, a corresponding application program can be provided, which can be used to detect a process that performs a predetermined task. The target user may acquire the application before performing the predetermined work, and may install the application into a terminal device (such as a mobile phone or a tablet computer). When a target user needs to execute a predetermined work, the application program may be logged in through an account, at this time, the terminal device may start its own sensor and corresponding component, the target user may carry the terminal device to execute the predetermined work, and in the process of executing the predetermined work, corresponding data is acquired through its own sensor and corresponding component, for example, the data may include environmental data of a position where the target user is located or its surroundings, behavior data of the target user, network connection information of the target user in the process of executing the predetermined work, and the like in the process of executing the predetermined work.

In step S104, authenticity detection is performed on the process of the target user performing the predetermined work according to a predetermined detection rule and the target data to determine whether the process of the target user performing the predetermined work is authentic.

The detection rule may be a rule for determining the authenticity of a process in which a user performs a predetermined work in a predetermined work process, and the detection rule may be determined in a variety of different manners, for example, may be determined by a preset model or algorithm, or may be determined according to empirical data related to performing the predetermined work process, or may be determined by statistical analysis according to historical data, and the like, and may be specifically set according to an actual situation, which is not limited in the embodiments of the present specification. The detection rules may include, for example, a range of variation in acceleration of the user, a range of variation in the number of network connections, a range of variation in altitude, etc.

In implementation, a detection rule may be preset, and the detection rule may determine a specific detection rule based on a process of performing a predetermined work and the predetermined work, for example, for the fire inspection or the road safety inspection, a corresponding detection rule may be determined through statistical analysis of historical data, and specifically, a normative or standard detection route and a detection step may be preset, and then, a worker may carry the detection device or the terminal device to perform the work such as the fire inspection or the road safety inspection according to the normative or standard detection step, and meanwhile, in the process of performing the work, the detection device or the terminal device may start its own sensor and corresponding component and obtain corresponding data through its own sensor and corresponding component, and specifically, the worker may perform the predetermined work, environmental data of the position where the worker is located or the surrounding of the worker, behavior data of the worker, network connection information of the worker in the process of executing the preset work, and the like. The obtained data can be used as reference data, and statistical analysis can be performed on the reference data to obtain a corresponding detection rule. The preset working process can be executed for a plurality of times by a plurality of different workers, and the data acquired for a plurality of times is used as the reference data. Different working processes can have different detection rule determination modes, different detection rule contents and the like.

After the detection rule is determined in the above manner, the acquired target data may be matched and compared with the detection rule, so as to perform authenticity detection on a process in which a target user performs predetermined work, if the acquired target data matches with the detection rule, it is indicated that the process in which the target user performs the predetermined work is real, at this time, it may be determined that the process in which the target user performs the predetermined work is real, if data in which the detection rule does not match exists in the acquired target data, it is indicated that the process in which the target user performs the predetermined work is not real, at this time, data in which the target user is rejected from performing the predetermined work may be stored in a predetermined block link point, and the target user may be prompted to re-perform the predetermined work, and the like.

In step S106, if it is determined that the process of the target user performing the predetermined work is real, data of the target user performing the predetermined work among the target data is stored to a predetermined blockchain node.

The data of the target user for executing the predetermined work may include operation data of the target user in the process of executing the predetermined work, time corresponding to each operation, time interval between different operations, work start time, work end time and the like.

In implementation, it is determined through the processing in step S104 that the process of performing the predetermined work by the target user is real, and the real work data can be stored in the blockchain.

The embodiment of the specification provides a data processing method applied to a block chain, which includes the steps of acquiring target data detected by a target user in the process of executing a preset work, wherein the target data includes one or more of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work, then, according to a preset detection rule and the target data, performing authenticity detection on the process of executing the preset work by the target user to determine whether the process of executing the preset work by the target user is real, and if the process of executing the preset work by the target user is determined to be real, storing data of executing the preset work by the target user in the target data to a preset block chain link point, so that the environment data detected by the target user in the process of executing the preset work, The behavior data and the network connection information of the target user are matched with a preset detection rule to judge the authenticity of the process of executing the preset work by the target user, and under the real condition, the data of executing the preset work by the target user in the target data is stored in a preset block chain node, so that whether the preset work is executed by the target user can be judged based on the detected data, the high positioning precision is not needed, and the flexibility of work authenticity detection is high.

Example two

As shown in fig. 2, an embodiment of the present specification provides a data processing method applied to a blockchain, where an execution subject of the method may be a terminal device or a server, where the terminal device may be a mobile terminal device such as a mobile phone or a tablet computer, and may also be a device such as a personal computer. The server may be an independent server or a server cluster including a plurality of servers. The method can be used in processes such as determining the authenticity of certain data before storing the data in the blockchain. In order to improve processing efficiency, the execution in the embodiments of the present specification is described by taking a server as an example, and for the case where the execution subject is a terminal device, the execution may be performed based on the server, which is not described herein again. The method may specifically comprise the steps of:

in step S202, data detected by the user in performing a predetermined work is acquired, and the acquired data is taken as sample data.

The sample data may include, for example, one or more of data for a wireless connection, position data, barometric pressure data, acceleration data, altitude data, time, and angular velocity data.

In implementation, during the execution of the predetermined work, one or more detection points (i.e., predetermined detection points, which may be located at a specified location) may be preset, and a graphic code or a near field communication tag or the like may be set at each predetermined detection point, where the graphic code may include a barcode or a two-dimensional code or the like, and the near field communication tag may include, for example, an NFC tag or the like. When the user passes the detection point or arrives at a designated location, the graphic code may be scanned or a near field communication tag (e.g., an NFC tag) may be approached by a detection device or terminal device to cause the detection device or terminal device to record that the user has arrived at the designated location or detection point.

The method comprises the steps of presetting a standard or standard detection route and detection steps, then carrying detection equipment or terminal equipment by a user to execute works such as fire inspection or road safety inspection according to the standard or standard detection steps, meanwhile, in the process of executing the works, starting a sensor and a corresponding component of the detection equipment or terminal equipment, and acquiring corresponding data through the sensor and the corresponding component of the detection equipment or terminal equipment, wherein the data specifically comprises position data of a worker or environmental data around the worker, behavior data of the worker, network connection information of the worker in the process of executing the preset works and the like. In addition, when the user passes through a predetermined detection point, the graphic code can be scanned or the detection device or the terminal device approaches the near field communication tag, and the detection device or the terminal device can record the current behavior of the user and corresponding data. After the user performs the predetermined work, the data recorded in the detection device or the terminal device may be acquired, and the acquired data may be used as sample data.

In the process that a user carries detection equipment or terminal equipment to execute works such as fire inspection, road safety inspection and the like according to standard or standard detection steps, the whole process of executing the preset work can be recorded, so that the process of executing the preset work can be clearer and more definite, and the following step S204 can be specifically participated in.

In step S204, video data recorded by the user during execution of a predetermined job is acquired.

In step S206, based on the sample data and the recorded video data, a process of the user performing a predetermined job is divided into one or more events in different scenes.

Wherein the events in the plurality of different scenarios include a login event and an event for detecting different scheduled detection points to which the user executed the scheduled job belongs.

In implementation, a user can log in the detection system through an account, a password and the like as one event, and the user can detect each detection point as one event, so that a process of the user executing a predetermined work can be formed through the events. Based on the above content, the process of the user executing the predetermined work can be divided into one or more events in different scenes through the sample data and the recorded video data, that is, the events include an event that the user logs in the detection system through an account number, a password and the like, an event that the user detects at each detection point, and the like.

It should be noted that detecting the predetermined detection point may include scanning a graphic code or detecting a near field communication tag based on near field communication (for example, approaching an NFC component of the detection device or the terminal device to the NFC tag to enable the NFC component to respond), for example, taking fire inspection as an example, that is, inspecting a fire-fighting device on each floor in a certain residential building, where the fire-fighting device on each floor is provided with a graphic code or an NFC tag, and a user carrying the detection device or the terminal device arrives at the fire-fighting device on each floor to scan the graphic code or detect the NFC tag, and after the detection is successful, the user goes to the fire-fighting device on the next floor to perform detection, and the like.

In step S208, for one or more events in different scenarios, statistical analysis is performed on the sample data, and data distribution and change of the sample data during the predetermined work process performed by the user are determined.

In implementation, for an event that a user logs in a detection system through an account, a password, and the like, environment data, behavior data of the user, network connection information, and the like in the event may be counted, for example, the number of WiFi and bluetooth lists, the number of steps of changes in air pressure and height, the number of acceleration behaviors, and the like in the event are counted, and then the data in the event is analyzed and summarized to obtain a corresponding analysis result. For an event detected by a user at a first predetermined detection point, environmental data, user behavior data, network connection information and the like under the event can be counted, and then the data under the event is analyzed and summarized to obtain a corresponding analysis result. For an event detected by a user at a second predetermined detection point, environment data, user behavior data, network connection information and the like under the event can be counted, then, the data under the event is analyzed and summarized to obtain a corresponding analysis result, and by analogy, a statistical analysis result corresponding to the event detected by the user at each predetermined detection point can be obtained, then, the statistical analysis result corresponding to each event can be further subjected to statistical analysis to obtain data distribution and change conditions (namely the data distribution and the data change conditions in the whole process) of the sample data during the predetermined working process executed by the user, for example, a value range of the event number, a gas pressure and height change range, an acceleration behavior number range, a value range of WiFi and bluetooth intersection in multiple events and the like.

In step S210, a detection rule corresponding to the process of the user performing the predetermined work is determined based on the data distribution and the variation of the sample data during the process of the user performing the predetermined work.

The detection rule comprises one or more of a range of the number of events, a time range of duration of each event, a variation range of the environment data, a range of the number of behaviors corresponding to the behavior data of the user and a range of the number of network connections corresponding to the network connection information.

In implementation, the data distribution and the variation of the sample data during the execution of the predetermined work by the user may be determined as the detection rule corresponding to the process of the user performing the predetermined work, or the data distribution and the variation of the sample data during the execution of the predetermined work by the user may be analyzed (for example, duplicate data is removed, invalid data is removed, and the like), so as to determine the detection rule corresponding to the process of the user performing the predetermined work.

After determining the detection rule corresponding to the process of executing the predetermined work by the user in the above manner, the authenticity detection may be performed on the process of executing the predetermined work by the target user, which may be specifically referred to in the following processing of step S212 to step S216.

In step S212, target data detected by the target user during the execution of the predetermined work is obtained, where the target data includes one or more of environment data of the target user during the execution of the predetermined work, behavior data of the target user, and network connection information of the target user during the execution of the predetermined work.

Wherein the target data may include one or more of wirelessly connected data, position data, barometric pressure data, acceleration data, altitude data, time, and angular velocity data.

In step S214, the process of the target user performing the predetermined work is subjected to authenticity detection according to the predetermined detection rule and the target data to determine whether the process of the target user performing the predetermined work is authentic.

The detection rule comprises one or more of a range of the number of events, a time range of duration of each event, a variation range of the environment data, a range of the number of behaviors corresponding to the behavior data of the target user, and a range of the number of network connections corresponding to the network connection information.

In step S216, if it is determined that the process of the target user performing the predetermined work is real, data of the target user performing the predetermined work among the target data is stored to a predetermined blockchain node.

The data is stored in the predetermined block chain nodes, and the data value is not questioned after the data is stored in the predetermined block chain nodes, and the data is tamper-proof, anti-counterfeiting and traceable. In order to make the data for executing the predetermined work process relatively complete, the data such as the environment data, the behavior data of the target user, and the network connection information in the target data can be stored in the predetermined block chain node, and the reliability of the data can be further improved.

In step S218, the environmental data, the behavior data of the target user, and the network connection information in the target data are stored to a predetermined blockchain node.

EXAMPLE III

As shown in fig. 3, an embodiment of the present specification provides a data processing method applied to a blockchain, where an execution subject of the method may be a terminal device or a server, where the terminal device may be a mobile terminal device such as a mobile phone or a tablet computer, and may also be a device such as a personal computer. The server may be an independent server or a server cluster including a plurality of servers. The method can be used in processes such as determining the authenticity of certain data before storing the data in the blockchain. In order to improve processing efficiency, the execution in the embodiments of the present specification is described by taking a server as an example, and for the case where the execution subject is a terminal device, the execution may be performed based on the server, which is not described herein again. The method may specifically comprise the steps of:

in step S302, data detected by the user in performing a predetermined work is acquired, and the acquired data is taken as sample data.

In practice, the data detected by the user in performing the predetermined work process may be obtained from the user in a variety of ways, for example, the data detected by the user in performing the predetermined work process may be purchased from the user, or the user may be invited to experience performing the predetermined work process in a rewarding manner, from which the data detected by the user in performing the predetermined work process may be obtained, or tests may be opened to certain users to perform the predetermined work process, after a certain period of testing, the data detected by the user in performing the predetermined work process may be collected from the user, and so on. The obtained data may be used as sample data, wherein the user does not need to record video data during executing a predetermined work process, and the specific processing process of the user may refer to the related contents in the second embodiment, which is not described herein again.

In step S304, based on the sample data, the process of the user performing the predetermined work is divided into one or more events in different scenes.

Wherein the events in the plurality of different scenarios include a login event and an event for detecting different scheduled detection points to which the user executed the scheduled job belongs. The preset detection point is provided with a graphic code or a near field communication tag, and the detection of the preset detection point comprises scanning of the graphic code or detection of the near field communication tag based on near field communication.

In step S306, for one or more events in different scenes, statistical analysis is performed on the sample data, and data distribution and change of the sample data during the predetermined work process performed by the user are determined.

In step S308, a detection rule corresponding to the process of the user performing the predetermined work is determined based on the data distribution and the variation of the sample data during the process of the user performing the predetermined work.

After determining the detection rule corresponding to the process of the user performing the predetermined work in the above manner, the authenticity detection may be performed on the process of the target user performing the predetermined work, which may be specifically referred to in the following processing from step S310 to step S314.

In step S310, target data detected by the target user during the execution of the predetermined work is obtained, where the target data includes one or more of environment data of the target user during the execution of the predetermined work, behavior data of the target user, and network connection information of the target user during the execution of the predetermined work.

In step S312, according to the predetermined detection rule and the target data, the process of the target user performing the predetermined work is subjected to authenticity detection to determine whether the process of the target user performing the predetermined work is authentic.

In step S314, if it is determined that the process of the target user performing the predetermined work is real, data of the target user performing the predetermined work among the target data is stored to a predetermined blockchain node.

In step S316, the environmental data, the behavior data of the target user, and the network connection information in the target data are stored to a predetermined blockchain node.

Example four

In this embodiment, a detailed description will be given to a data processing method applied to a block chain provided in this specification, in combination with a specific application scenario, where the corresponding application scenario is an application scenario in which inspection is performed on fire fighting equipment on each floor in a certain residential building (i.e., fire inspection), and the predetermined work may be fire inspection work. As shown in fig. 4, the main body of the method may be a terminal device or a server, where the terminal device may be a mobile terminal device such as a mobile phone or a tablet computer, or may be a device such as a personal computer. The server may be an independent server or a server cluster including a plurality of servers. The method can be used in processes such as determining the authenticity of certain data before storing the data in the blockchain. In order to improve processing efficiency, the execution in the embodiments of the present specification is described by taking a server as an example, and for the case where the execution subject is a terminal device, the execution may be performed based on the server, which is not described herein again. The method may specifically comprise the steps of:

in step S402, data detected by the user during the fire inspection work is acquired, and the acquired data is used as sample data.

In practice, as shown in fig. 5, during the fire patrol work, one or more detection points (i.e., predetermined detection points) may be set in advance at the positions where the fire fighting equipment is placed in each floor, and a graphic code, an NFC tag, or the like may be set at each predetermined detection point.

The method comprises the steps of presetting a standard or standard detection route and a detection step, then carrying detection equipment or terminal equipment by a user to execute fire inspection work according to the standard or standard detection step, meanwhile, in the process of executing the work, the detection equipment or the terminal equipment can start a sensor and a corresponding component of the detection equipment or the terminal equipment, and acquire corresponding data through the sensor and the corresponding component of the detection equipment or the terminal equipment, and specifically, the method can comprise the steps of executing the preset work by a worker, locating position of the worker or environmental data around the worker, behavior data of the worker, network connection information of the worker in the preset work process and the like. In addition, when the user passes through a predetermined detection point, the graphic code can be scanned or the detection device or the terminal device approaches the near field communication tag, and the detection device or the terminal device can record the current behavior of the user and corresponding data. After the user performs the predetermined work, the data recorded in the detection device or the terminal device may be acquired, and the acquired data may be used as sample data.

In the process that a user carries detection equipment or terminal equipment to execute fire protection inspection work according to standard or standard detection steps, the whole process of executing the fire protection inspection work can be recorded, so that the process of executing the fire protection inspection work is clearer and more definite, and the following step S404 can be specifically participated in.

In step S404, video data of the recorded user during the working process of the fire inspection tour is acquired.

In step S406, based on the sample data and the recorded video data, the process of the fire protection inspection work of the user is divided into one or more events in different scenes.

The events in the plurality of different scenes comprise login events and events detected aiming at different preset detection points (namely, positions for placing fire-fighting equipment in each floor in the figure 5) of the fire-fighting patrol work of the user.

In implementation, as shown in fig. 5, a user inspects fire-fighting equipment on each floor in a certain residential building, a graphic code or an NFC tag is arranged at the fire-fighting equipment on each floor, the user carries detection equipment or terminal equipment to reach the fire-fighting equipment on each floor to scan the graphic code or detect the NFC tag, and after the detection is successful, the user goes to the fire-fighting equipment on the next floor to perform detection and the like.

In step S408, for one or more events in different scenes, statistical analysis is performed on the sample data, and data distribution and change conditions of the sample data in the working process of the fire protection inspection by the user are determined.

In step S410, a detection rule corresponding to the user in the process of the fire protection inspection work is determined based on the data distribution and the change condition of the sample data of the user in the process of the fire protection inspection work.

After determining the detection rule corresponding to the process of the fire inspection work of the user in the above manner, the authenticity detection may be performed on the process of the fire inspection work performed by the target user, which may be specifically referred to in the following processing from step S412 to step S416.

In step S412, target data detected by the target user in the fire inspection work process is obtained, where the target data includes one or more of environment data of the target user in the fire inspection work process, behavior data of the target user, and network connection information of the target user in the fire inspection work process.

In step S414, the authenticity of the process of the target user performing the fire inspection work is detected according to the predetermined detection rule and the target data to determine whether the process of the target user performing the fire inspection work is authentic.

In step S416, if it is determined that the process of the target user performing the fire inspection work is real, data of the target user performing the fire inspection work among the target data is stored to a predetermined block chain node.

The data is stored in the predetermined block chain nodes, and the data value is not questioned after the data is stored in the predetermined block chain nodes, and the data is tamper-proof, anti-counterfeiting and traceable. In order to ensure that the data for executing the fire protection inspection work process is relatively complete, the environmental data, the behavior data of the target user, the network connection information and other data in the target data can be stored in the preset block chain node, and the reliability of the data can be further improved.

In step S418, the environmental data, the behavior data of the target user, and the network connection information in the target data are stored to a predetermined blockchain node.

EXAMPLE five

Based on the same idea, the data processing method applied to the block chain provided in the embodiment of the present specification further provides a data processing apparatus applied to the block chain, as shown in fig. 6.

The data processing device applied to the block chain comprises: a data acquisition module 601, an authenticity judgment module 602, and a storage module 603, wherein:

a data obtaining module 601, configured to obtain target data detected by a target user in a process of executing a predetermined work, where the target data includes one or more of environment data of the target user in the process of executing the predetermined work, behavior data of the target user, and network connection information of the target user in the process of executing the predetermined work;

the authenticity judgment module 602 is configured to perform authenticity detection on a process in which the target user performs predetermined work according to a predetermined detection rule and the target data, so as to determine whether the process in which the target user performs the predetermined work is authentic;

a storage module 603, configured to store, in the target data, data of the target user performing a predetermined job to a predetermined blockchain node if it is determined that the process of the target user performing the predetermined job is real.

In an embodiment of this specification, the apparatus further includes:

In an embodiment of this specification, the event partitioning module includes:

In this embodiment of the present specification, the data of the target user performing the predetermined work includes events in the one or more different scenarios and execution times of the events in the one or more different scenarios, and the apparatus further includes:

In an embodiment of this specification, the detection rule includes one or more of a range of the number of events, a time range of duration of each event, a variation range of the environment data, a range of a number of behaviors corresponding to behavior data of the target user, and a range of a number of network connections corresponding to the network connection information.

In this embodiment of the present specification, the events in the plurality of different scenarios include a login event and an event for detecting different predetermined detection points to which the user performs a predetermined job.

In an embodiment of this specification, the predetermined detection point is provided with a graphic code or a near field communication tag, and detecting the predetermined detection point includes scanning the graphic code or detecting the near field communication tag based on near field communication.

In an embodiment of the present description, the target data comprises one or more of wirelessly connected data, position data, barometric pressure data, acceleration data, altitude data, time and angular velocity data.

The embodiment of the specification provides a data processing device applied to a block chain, which is used for acquiring target data detected by a target user in the process of executing a preset work, wherein the target data comprises one or more of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work, then, according to a preset detection rule and the target data, authenticity detection is carried out on the process of executing the preset work by the target user to determine whether the process of executing the preset work by the target user is real, and if the process of executing the preset work by the target user is determined to be real, data of executing the preset work by the target user in the target data is stored to a preset block chain link point, so that the environment data detected by the target user in the process of executing the preset work, The behavior data and the network connection information of the target user are matched with a preset detection rule to judge the authenticity of the process of executing the preset work by the target user, and under the real condition, the data of executing the preset work by the target user in the target data is stored in a preset block chain node, so that whether the preset work is executed by the target user can be judged based on the detected data, the high positioning precision is not needed, and the flexibility of work authenticity detection is high.

EXAMPLE six

Based on the same idea, the data processing apparatus applied to the block chain provided in the embodiment of the present specification further provides a data processing device applied to the block chain, as shown in fig. 7.

The data processing device applied to the blockchain may be the server provided in the above embodiments.

The data processing apparatus applied to the blockchain may have a large difference due to different configurations or performances, and may include one or more processors 701 and a memory 702, where one or more stored applications or data may be stored in the memory 702. Memory 702 may be, among other things, transient storage or persistent storage. The application program stored in memory 702 may include one or more modules (not shown), each of which may include a series of computer-executable instructions corresponding to a sequence of data processing devices applied to a blockchain. Still further, the processor 701 may be arranged in communication with the memory 702 to execute a series of computer executable instructions in the memory 702 on a data processing device applied to a blockchain. The data processing apparatus applied to the blockchain may also include one or more power supplies 703, one or more wired or wireless network interfaces 704, one or more input-output interfaces 705, one or more keyboards 706.

In particular, in this embodiment, the data processing apparatus applied to the blockchain includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions corresponding to the data processing apparatus applied to the blockchain, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

In the embodiment of this specification, the method further includes:

In this embodiment of the present specification, the dividing, based on the sample data, a process of the user performing a predetermined job into one or more events in different scenarios includes:

acquiring recorded video data of the user in a preset working process;

In this embodiment of the present specification, the data of the target user performing the predetermined work includes the event in the one or more different scenarios and the execution time of the event in the one or more different scenarios, and further includes:

The embodiment of the specification provides a data processing device applied to a block chain, which is used for acquiring target data detected by a target user in the process of executing a preset work, wherein the target data comprises one or more of environment data of the target user in the process of executing the preset work, behavior data of the target user and network connection information of the target user in the process of executing the preset work, then, according to a preset detection rule and the target data, authenticity detection is carried out on the process of executing the preset work by the target user to determine whether the process of executing the preset work by the target user is real or not, and if the process of executing the preset work by the target user is determined to be real, data of executing the preset work by the target user in the target data is stored to a preset block chain node, so that the environment data detected by the target user in the process of executing the preset work, The behavior data and the network connection information of the target user are matched with a preset detection rule to judge the authenticity of the process of executing the preset work by the target user, and under the real condition, the data of executing the preset work by the target user in the target data is stored in a preset block chain node, so that whether the preset work is executed by the target user can be judged based on the detected data, the high positioning precision is not needed, and the flexibility of work authenticity detection is high.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present description are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other data processing apparatus that is programmable to function in a blockchain, such that the instructions, which execute via the processor of the computer or other data processing apparatus that is programmable to function in a blockchain, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A data processing method applied to a blockchain, the method comprising:

2. The method of claim 1, further comprising:

3. The method of claim 2, said dividing a process of said user performing a predetermined job into one or more events under different scenarios based on said sample data, comprising:

acquiring recorded video data of the user in a preset working process;

4. The method of claim 2, the data of the target user performing the predetermined work comprising events under the one or more different scenarios and execution times of the events under the one or more different scenarios, the method further comprising:

5. The method of claim 4, wherein the detection rule comprises one or more of a range of the number of events, a time range of each event duration, a variation range of the environmental data, a range of a number of behaviors corresponding to the behavior data of the target user, and a range of a number of network connections corresponding to the network connection information.

6. The method of claim 2, the events in the plurality of different scenarios comprising a login event and an event detecting a different predetermined detection point to which the user performed a predetermined job.

7. The method of claim 6, the predetermined detection point being provided with a graphical code or a near field communication tag, the detecting of the predetermined detection point comprising scanning the graphical code or detecting the near field communication tag based on near field communication.

8. The method of claim 1, the target data comprising one or more of wirelessly connected data, position data, barometric data, acceleration data, altitude data, time, and angular velocity data.

9. A data processing apparatus for application to a blockchain, the apparatus comprising:

10. The apparatus of claim 9, the apparatus further comprising:

11. The apparatus of claim 10, the event partitioning module, comprising:

12. The apparatus of claim 10, the data of the target user performing the predetermined work comprising events under the one or more different scenarios and execution times of the events under the one or more different scenarios, the apparatus further comprising:

13. A data processing apparatus applied to a blockchain, the data processing apparatus applied to a blockchain comprising:

a processor; and