CN112632058A

CN112632058A - Track determination method, device and equipment and storage medium

Info

Publication number: CN112632058A
Application number: CN201910906931.0A
Authority: CN
Inventors: 章超; 曾挥毫; 李林森; 金兵兵
Original assignee: Hangzhou Hikvision Digital Technology Co Ltd
Current assignee: Hangzhou Hikvision Digital Technology Co Ltd
Priority date: 2019-09-24
Filing date: 2019-09-24
Publication date: 2021-04-09

Abstract

The invention provides a track determination method, a track determination device, track determination equipment and a storage medium, wherein the method comprises the following steps: obtaining data attributes from different acquisition devices, the data attributes including at least: acquiring position information of equipment, an object identification ID of an object acquired by the acquisition equipment and a timestamp of the acquired object; storing the data attributes in a first database table; when the track needs to be generated, determining a time period T1 required by searching the data attributes, searching the data attributes with the time stamps in the time period T1 from the first database table, finding out the data attributes containing the same object ID from the found data attributes, and sequencing the position information in the found data attributes according to the time stamps to obtain the object track.

Description

Track determination method, device and equipment and storage medium

Technical Field

The invention relates to the technical field of security protection, in particular to a track determination method, a track determination device, track determination equipment and a storage medium.

Background

The problem faced currently is how to utilize video and other perception data to trace the action track of an object such as a vehicle in the physical world and the behavior pattern analysis on the object, under the background of tens of thousands of perception data, so as to dig out some useful information.

In the related track determining method, the track of the object is determined mainly based on GPS data reported by the object (for example, reported by a vehicle through a GPS), and in a scene in which the GPS data cannot be acquired, the track of the object cannot be determined through the method, so the track determining method is limited only to a GPS data acquisition scene, and an application scene is limited.

Disclosure of Invention

In view of this, the present invention provides a method, an apparatus, and a storage medium for determining a trajectory of an object, which can determine the trajectory of the object by using location information of a collection device without collecting GPS data.

The first aspect of the present invention provides a trajectory determination method, including:

obtaining data attributes from different acquisition devices, the data attributes including at least: acquiring position information of equipment, an object identification ID of an object acquired by the acquisition equipment and a timestamp of the acquired object;

storing the data attributes in a first database table;

when the track needs to be generated, determining a time period T1 required by searching the data attributes, searching the data attributes with the time stamps in the time period T1 from the first database table, finding out the data attributes containing the same object ID from the found data attributes, and sequencing the position information in the found data attributes according to the time stamps to obtain the object track.

According to one embodiment of the invention, storing the data attributes in a first database table comprises:

and storing the data attributes into a target partition corresponding to the time period of the timestamp in the first database table according to the timestamp in the data attributes.

According to an embodiment of the present invention, storing the data attribute in a target partition corresponding to a time period in which a timestamp is located in the first database table according to the timestamp in the data attribute includes:

determining a data identifier of the data attribute according to the time stamp in the data attribute and the obtained hash code;

determining a partition corresponding to the data identifier of the data attribute in the first database table as a target partition;

and storing the data attribute and the data identification in the target partition in an associated manner.

According to an embodiment of the present invention, after the position information in the found data attribute is sorted according to the time stamp to obtain the object track, the method further includes:

distributing corresponding track marks for the object tracks;

and storing the object track into a second database table, and establishing a corresponding index for the track identification in a specified search engine cluster.

According to an embodiment of the invention, the method further comprises:

receiving an externally input query condition;

inquiring a target index meeting the inquiry condition from the specified search engine cluster according to the inquiry condition, and searching an object track corresponding to the track identification in the second database table according to the track identification corresponding to the target index;

and acquiring a target object track meeting the query condition from the found object tracks.

In accordance with one embodiment of the present invention,

the index includes: the maximum timestamp and the minimum timestamp in the data attribute of each position information on the object track and the object ID;

the appointed search engine cluster comprises at least two index storage segments, each index storage segment has a corresponding time period, and the maximum time stamp and the minimum time stamp in the index stored in each index storage segment are both in the time period corresponding to the index storage segment;

the query conditions at least include: target object ID, target time period;

inquiring the target index meeting the inquiry condition from the specified search engine cluster according to the inquiry condition, wherein the method comprises the following steps:

inquiring a target index storage segment from the specified search engine cluster according to the target time segment, wherein the time segment corresponding to the target index storage segment is intersected with the target time segment;

searching an index containing the ID of the target object in the target index storage segment;

and aiming at each found index, checking whether the maximum time stamp and/or the minimum time stamp in the index is within the target time period, if so, taking the index as the target index.

In accordance with one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

searching an index containing a target object ID in the appointed search engine cluster;

In accordance with one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

obtaining a target object track meeting the query condition from the found object tracks, including:

checking whether the number of the searched object tracks is more than two;

if so, if at least two object tracks corresponding to the same target object ID exist in the searched object tracks, splicing the object tracks corresponding to the same target object ID in the searched object tracks, intercepting a first track section from the spliced object tracks, wherein the time stamp of the data attribute of each position information on the first track section is in the target time period, and determining the first track section as the target track.

A second aspect of the present invention provides a trajectory determination device, including:

a data attribute obtaining module, configured to obtain data attributes from different acquisition devices, where the data attributes at least include: acquiring position information of equipment, an object identification ID of an object acquired by the acquisition equipment and a timestamp of the acquired object;

the data attribute storage module is used for storing the data attributes into a first database table;

and the object track generation module is used for determining a time period T1 required by searching the data attributes when the track needs to be generated, searching the data attributes with the time stamps in the time period T1 from the first database table, finding out the data attributes containing the same object ID from the searched data attributes, and sequencing the position information in the found data attributes according to the time stamps to obtain the object track.

According to an embodiment of the present invention, when the data attribute storage module stores the data attribute in the first database table, the data attribute storage module is specifically configured to:

According to an embodiment of the present invention, when the data attribute storage module stores the data attribute into the target partition corresponding to the time period of the timestamp in the first database table according to the timestamp in the data attribute, the data attribute storage module is specifically configured to:

According to an embodiment of the present invention, after the object trajectory generating module, the apparatus further includes:

a track identifier determining module, configured to allocate a corresponding track identifier to the object track;

and the object track storage module is used for storing the object track into a second database table and establishing a corresponding index for the track identifier in a specified search engine cluster.

According to an embodiment of the invention, the apparatus further comprises:

the query condition receiving module is used for receiving externally input query conditions;

an object track searching module, configured to search a target index that meets the query condition from the specified search engine cluster according to the query condition, and search an object track corresponding to a track identifier in the second database table according to the track identifier corresponding to the target index;

and the target object track determining module is used for acquiring the target object track meeting the query condition from the searched object tracks.

In accordance with one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

when the object trajectory searching module queries the target index satisfying the query condition from the specified search engine cluster according to the query condition, the object trajectory searching module is specifically configured to:

In accordance with one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

In accordance with one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

when the target object trajectory determination module obtains a target object trajectory satisfying the query condition from the found object trajectories, the target object trajectory determination module is specifically configured to:

checking whether the number of the searched object tracks is more than two;

A third aspect of the invention provides an electronic device comprising a processor and a memory; the memory stores a program that can be called by the processor; wherein the processor, when executing the program, implements the trajectory determination method as described in the foregoing embodiments.

A fourth aspect of the present invention provides a machine-readable storage medium on which a program is stored, which, when executed by a processor, implements the trajectory determination method as described in the foregoing embodiments.

The embodiment of the invention has the following beneficial effects:

in the embodiment of the invention, data attributes acquired from different acquisition devices are stored in a first database table, when a track needs to be generated, a time period T1 required for searching the data attributes is determined, a data attribute with a timestamp in the time period T1 can be searched from the first database table, position information in the data attribute of the same object ID in the searched data attributes is sorted according to the timestamp, an object track corresponding to the object ID can be obtained, the acquisition device acquires the object, and the object passes through the acquisition device, so that the position information of the acquisition device can be regarded as a track point of the object, the obtained object track corresponding to the object ID can be regarded as the track of the object identified by the object ID, the object track can be generated by using the position information of the acquisition device, GPS data does not need to be acquired, and an application scene is not limited by a GPS.

Drawings

FIG. 1 is a schematic flow chart diagram of a trajectory determination method according to an embodiment of the invention;

FIG. 2 is a block diagram of a trajectory determination device according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart diagram of a trajectory determination method according to another embodiment of the present invention;

FIG. 4 is a schematic flow chart diagram illustrating a trajectory determination method according to another embodiment of the present invention;

fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one type of device from another. For example, a first device may also be referred to as a second device, and similarly, a second device may also be referred to as a first device, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

In order to make the description of the present invention clearer and more concise, some technical terms in the present invention are explained below:

ES: the system is called an elastic search, and is an open-source and real-time distributed search and analysis engine, and indexes in the elastic search are non-relational databases and are used for storing data.

HBase: a distributed, column-oriented, open-source, non-relational database.

Region: the basic unit of HBase data management, namely the management of data in HBase, is performed by taking Region as a unit.

rowKey: the HBase is the unique identification of a piece of data, namely the key word of the data.

Spark: a fast general purpose computing engine designed specifically for large scale data processing.

Kafka: a high throughput, distributed publish-subscribe messaging system.

The following describes the trajectory determination method according to the embodiment of the present invention more specifically, but not limited thereto.

In one embodiment, referring to fig. 1, a trajectory determination method may include the steps of:

s100: obtaining data attributes from different acquisition devices, the data attributes including at least: acquiring position information of equipment, an object identification ID of an object acquired by the acquisition equipment and a timestamp of the acquired object;

s200: storing the data attributes in a first database table;

s300: when the track needs to be generated, determining a time period T1 required by searching the data attributes, searching the data attributes with the time stamps in the time period T1 from the first database table, finding out the data attributes containing the same object ID from the found data attributes, and sequencing the position information in the found data attributes according to the time stamps to obtain the object track.

The execution main body of the track determination method of the embodiment of the invention can be electronic equipment, the electronic equipment can be a server formed by computer equipment, and the performance of the computer equipment can be configured according to the requirement. Of course, the specific type of the electronic device is not limited as long as it has certain data processing capability.

In step S100, data attributes are obtained from different acquisition devices, where the data attributes at least include: the method comprises the steps of collecting position information of equipment, an object identification ID of an object collected by the collecting equipment and a time stamp of the collected object.

The acquisition equipment can comprise video monitoring equipment, a face snapshot camera, access control equipment and the like, and is determined according to application scenes. The application scenario of the embodiment of the present invention may be, for example, a checkpoint monitoring scenario, in which each checkpoint is provided with at least one acquisition device, the acquisition devices may acquire vehicle passing data of a vehicle, and the data attribute may be extracted from the acquired vehicle passing data.

The acquisition devices at different bayonet positions have fixed point positions in a bayonet monitoring scene, so that the position information of each acquisition device is fixed and can be configured in the acquisition device in advance. Of course, the bayonet monitoring scene is only an example, and the embodiment of the present invention may also be applied to other scenes including a plurality of acquisition devices distributed at different points, such as a garden and other scenes.

When an object is acquired by an acquisition device, a data attribute at least including location information of the acquisition device, an object ID of the object acquired by the acquisition device, and a time stamp of the object acquired can be determined. The acquisition device acquires an object, which indicates that the object passes through the acquisition device when being acquired, and the distance between the object and the acquisition device is very close, so that the position information of the acquisition device can be regarded as a track point of the object.

The position information of the acquisition device may be represented by a position identifier of the acquisition device in the scene, or may be directly represented by a geographic position, which is not limited specifically. The object ID may be used to identify an object collected by the collection device, and may be a name, a license plate number, or the like, which is not limited thereto. The object may refer to a person, a motor vehicle, a non-motor vehicle, etc. The time stamp indicates the time the object passed the acquisition device.

When the data attribute is acquired, the data attribute acquired by different acquisition devices can be specifically acquired by Kafka. Kafka can format heterogeneous data into data attributes in a unified format, and data volume collected at different time every day is different, for example, a lot of vehicle passing data are collected by a bayonet in the daytime, while less vehicle passing data are collected at night, and the data attributes are cached in Kafka, so that a data buffering effect can be achieved, and data can be slowly consumed.

When the data attributes are consumed from Kafka, the data attributes may be cleaned first, and data attributes lacking key information, such as data attributes lacking any one of the object ID, the timestamp, and the location information of the collection device, may be removed.

In step S200, the data attributes are stored in a first database table.

Data attributes with complete key information, i.e., object ID, timestamp, and location information, that are retained after Kafka cleansing may be stored in a first database table.

The first database table may be one table in a designated distributed database cluster, which may be an HBase. When data is stored in the HBase, each piece of data needs to have a keyword rowkey. Therefore, when the data attributes are stored in the first database table, one rowkey can be created for the data attributes, and the rowkey is stored in the first database table in association with the data attributes, wherein each rowkey is unique in the first database table, and the specific manner is not limited.

In step S300, when a track needs to be generated, determining a time period T1 required for searching for data attributes, searching for data attributes with a timestamp in the time period T1 from the first database table, finding out data attributes containing the same object ID from the found data attributes, and sorting the position information in the found data attributes according to the timestamp to obtain an object track.

Step S300 may be triggered by a track generation instruction, and when the track generation instruction is received, it is determined that a track needs to be generated. The trajectory generation indication may be input by a user or generated by the system. For example, the system may trigger the track generation indication once a day, thereby performing the track generation operation once a day.

The time period T1 is not limited, for example, the time period T1 may be a time period before the time period of the current system time. The time period may be divided by week, and accordingly, the time period T1 includes the last week of the week in which the current system time is located, and/or the last week, etc. The number and length of each determined time period T1 can be configured as desired.

For example, the current system time is in the time period of 2018-08-30-00 to 2018-09-06-00, the time period of 2018-08-23-00 to 2018-08-30-00 can be determined as the time period T1, and the time periods of 2018-08-16-00 to 2018-08-23-00, or 2018-08-09-00 to 2018-08-16-00, which are earlier than the time periods of 2018-08-30-00 to 2018-09-06-00, can also be used as the time period T1, which is only by way of example and is not limited herein.

Optionally, when the time period T1 is determined, it may be checked whether the time period T1 is within the time range in the configuration file, if so, the subsequent steps are continued, otherwise, the time period is over, and the execution may be ended. The time range may be, for example, a time range of one year, which the system runtime may call a configuration file to read.

Assuming that the time range in the configuration file is 2018-01-01-00 to 2019-01-01-00 and the time period T1 is 2018-08-23-00 to 2018-08-30-00, the time period T1 is determined to be within the time range, and the subsequent steps are continuously executed.

For each determined time period T1, data attributes with time stamps in the time period T1 are found from the first database table, for example, if the time period T1 is 2018-08-30-00 to 2018-09-06-00, data attributes with time stamps in the time periods 2018-08-30-00 to 2018-09-06-00 can be found from the first database table, data attributes containing the same object ID are found from the found data attributes, and the object tracks are obtained by sorting the position information in the found data attributes according to the time stamps. The sorting manner may be in the order from morning to evening according to the time stamps, or in the order from evening to morning, and is not limited specifically.

The position information of the acquisition equipment in the data attribute of the same object ID is in the same object track, and the position information of the acquisition equipment can indicate that the object moves to a position close to the acquisition equipment, so that the object track can be used as the track of the object identified by the object ID, and the object track is generated by using the position information of the acquisition equipment.

In addition, because the data attributes are acquired according to the time periods, the data attributes are segmented in time. If the track of an object is long, it may be cut into more than two segments (i.e. the track of the object is hashed). Therefore, the length of the generated object track can be ensured not to exceed the specified length, and the storage of the track is facilitated.

Optionally, step S300 may be implemented by using a Spark engine, which may improve data processing efficiency.

In one embodiment, the above method flow can be executed by a trajectory determination device, as shown in fig. 2, the trajectory determination device 100 mainly includes 3 modules: a data attribute acquisition module 101, a data attribute storage module 102 and an object trajectory generation module 103. The data attribute acquiring module 101 is configured to execute the step S100, the data attribute storing module 102 is configured to execute the step S200, and the object trajectory generating module 103 is configured to execute the step S300.

In one embodiment, the step S200 of storing the data attributes in a first database table includes:

Since the amount of data stored in the first database table may be large, in this embodiment, the partition (Region) in the first database table is divided by time period, so that all data attributes can be shared by the partitions corresponding to multiple time periods.

Each partition may correspond to a time period, although more than two partitions may correspond to the same time period. Preferably, the partitions in the first database table may be divided in months, each month corresponding to a plurality of partitions, each partition having a size of, for example, about 10 GB. In this manner, data corresponding to one month may be stored by multiple partitions.

In this embodiment, when storing the data attribute, the data attribute may be stored in the target partition corresponding to the time period in which the timestamp in the data attribute is located in the first database table, so that the data attribute in different time periods may be stored in different partitions according to the time period distribution, and when the data attribute is required, the data attribute may be searched in the partition corresponding to the corresponding time period without traversing the entire first database table.

In one embodiment, storing the data attribute in a target partition corresponding to a time period in which a timestamp is located in the first database table according to the timestamp in the data attribute includes:

s201: determining a data identifier of the data attribute according to the time stamp in the data attribute and the obtained hash code;

s202: determining a partition corresponding to the data identifier of the data attribute in the first database table as a target partition;

s203: and storing the data attribute and the data identification in the target partition in an associated manner.

When storing data attribute, firstly determining a data identifier for the data attribute, wherein the data identifier is used as a keyword of the data attribute in the first database table. After the data identifier of the data attribute is determined, the data attribute and the data identifier may be sent to a designated distributed database cluster, so that the designated distributed database cluster determines a partition corresponding to the data identifier of the data attribute in the first database table as a target partition, and stores the data attribute and the data identifier in the target partition in an associated manner.

Each time period in the first database table may correspond to at least two partitions, at least two partitions corresponding to the time period in which the timestamp is located may be determined according to the timestamp in the data identifier, and a target partition may be determined from the two partitions according to the hash code, so that a unique target partition may be determined according to the data identifier of the data attribute.

The data identifier of the data attribute may be composed of the content of the specified field of the timestamp in the data attribute and the obtained hash code, and may of course include other contents. Preferably, the specified field content may include year field content and month field content, and the obtained hash code may be randomly generated by a hash code algorithm.

Correspondingly, when the partition corresponding to the data identifier of the data attribute in the first database table is determined as the target partition, the partition corresponding to the specified field content in the data identifier of the data attribute in the first database table may be determined as the target partition, and the data attribute and the data identifier are stored in the target partition corresponding to the specified field content in an associated manner. In this way, the data attributes acquired in the same month in the same year can be randomly stored in one of several partitions corresponding to the month in the year.

In this embodiment, when designing the data identifier of the data attribute, a multi-bit hash code may be set. When the data attribute is stored every time, one hash code can be randomly acquired from the set hash codes to generate the data identifier of the data attribute, so that the data attribute of one month can be randomly dispersed into a plurality of partitions corresponding to the month, and the load balance of the whole cluster is facilitated.

In one embodiment, referring to fig. 3, after sorting the location information in the found data attribute according to the time stamp to obtain the object track, the method further includes the following steps:

s400: distributing corresponding track marks for the object tracks;

s500: and storing the object track into a second database table, and establishing a corresponding index for the track identification in a specified search engine cluster.

The second database table may be another table in a designated distributed database cluster, which may be an HBase. When data is stored in the HBase, each piece of data needs to have a keyword rowkey. Thus, an object track may be assigned a track identification as its rowkey when it is stored in the second database table.

The specific form and content of the track identifier are not limited, and the object track can be uniquely identified, and when the object track is stored in the second database table, the object track and the distributed track identifier can be stored in the second database table in an associated manner.

Because the object track generated by using the data attribute in the first database table is stored in the second database table, compared with the first database table, the number of the stored data in the second database table is less, the query quantity required by external query is reduced, and the track retrieval efficiency is improved.

Since the object tracks generated in step S300 are data attributes with time stamps within the time period T1, and the object tracks may not be complete within the time period T1, the object tracks stored in the second database table may be hashed, and if at least two hashed object tracks corresponding to the same object ID are found, the hashed object tracks corresponding to the same object ID may be concatenated.

When the object track is stored in the second database table, a corresponding index is established for the track identification of the object track in the designated search engine cluster, and the designated search engine cluster is, for example, ES. Therefore, when a user needs to query the track, the index can be searched in the designated search engine cluster, and the object track corresponding to the track identifier is obtained from the second database table according to the track identifier corresponding to the searched index.

In this embodiment, the object tracks are stored in the second database table of the designated distributed database cluster in a time slot, when the object tracks need to be queried, the object tracks can be searched and obtained from the existing object tracks in the second database table, and data attributes do not need to be obtained from the first database table for generation, so that the data query quantity can be reduced, the query efficiency of the object tracks can be improved, and due to the distributed characteristic of the designated distributed database cluster, the method is applicable to scenes with large data quantity or small data quantity, and has good expandability and fault tolerance.

In addition, a corresponding index is established in the designated search engine cluster for the track identification of the object track in the second database table. Therefore, the needed index can be found in the designated search engine cluster, the corresponding object track can be obtained from the second database table according to the track identification corresponding to the index, compared with the mode that the information such as the index is put in the memory and the needed index is found in the memory, the data size capable of being accommodated in the second database table is larger, the retrieval performance is not limited by the memory, the storage and the quick retrieval of mass track data can be realized, and the subsequent application such as data mining can be paved.

In one embodiment, referring to fig. 4, the trajectory determination method further comprises the steps of:

s600: receiving an externally input query condition;

s700: inquiring a target index meeting the inquiry condition from the specified search engine cluster according to the inquiry condition, and searching an object track corresponding to the track identification in the second database table according to the track identification corresponding to the target index;

s800: and acquiring a target object track meeting the query condition from the found object tracks.

The query conditions may be user input. Optionally, a query request for requesting to find a target object track, which is input by a user, may be received, and the query condition may be carried in the query request.

The query condition may be used to query the trajectory of the object, and the specific information may be set as required, for example, if it is desired to find the trajectory of an object within a certain period of time, the object identifier of the object and the required time period may be set; if all tracks of an object are to be found, the object identifier of the object can be set; if the trajectories of all objects within a certain period of time are to be found, then the required period of time can be set, and so on.

In this embodiment, although the query condition is used to find the target object track, the basis for finding the index is also the query condition. The query condition may be sent to a designated search engine cluster, and the designated search engine cluster searches locally for a target index satisfying the query condition.

In the query process, a target index meeting the query condition is queried from a specified search engine cluster according to the query condition, and then an object track corresponding to the track identification is obtained in the second database table according to the track identification corresponding to the target index.

Based on the description in the foregoing embodiment, the object tracks stored in the second database table are divided according to time periods, and the searched object tracks may not be the target object tracks directly. In one case, all object tracks found may be hashed object tracks of the object over a short period of time, while all that is needed is an object track of the object over a longer period of time; under another condition, the found object track contains redundant track points; of course, it is also possible for both of the above to occur simultaneously.

Therefore, a target object track satisfying the query condition needs to be obtained from the searched object tracks, for example, the searched object tracks may be subjected to processes such as splicing and intercepting to obtain a desired target object track.

In the embodiment, the index is stored in the designated search engine cluster, so that the target index can be searched in the designated search engine cluster during query, and the query speed and the number of the index are improved by utilizing the characteristics of large storage capacity and high search speed of the designated search engine cluster, and the storage and the quick retrieval of mass track related data can be realized.

In one embodiment, the index includes: the maximum timestamp and the minimum timestamp in the data attribute of each position information on the object track and the object ID. The maximum timestamp is the maximum timestamp in the timestamps of the data attributes of the respective location information on the object track, and the minimum timestamp is the minimum timestamp in the timestamps of the data attributes of the respective location information on the object track.

The appointed search engine cluster comprises at least two index storage segments, each index storage segment has a corresponding time period, and the maximum time stamp and the minimum time stamp in the index stored in each index storage segment are both in the corresponding time period of the index storage segment.

The query conditions at least include: target object ID, target time period.

In step S700, querying a target index satisfying the query condition from the specified search engine cluster according to the query condition includes:

s701: inquiring a target index storage segment from the specified search engine cluster according to the target time segment, wherein the time segment corresponding to the target index storage segment is intersected with the target time segment;

s702: searching an index containing the ID of the target object in the target index storage segment;

s703: and aiming at each found index, checking whether the maximum time stamp and/or the minimum time stamp in the index is within the target time period, if so, taking the index as the target index.

In order to prevent the problem that index query performance is reduced due to excessive data of a designated search engine cluster, a new index storage segment can be established every period of time, such as 7 days, the latest index is stored in the new index storage segment, and each index storage segment can store indexes acquired in one week, so that data can be stored to the maximum extent and performance can be improved.

In step S701, an index storage segment corresponding to a time segment having an intersection with the target time segment may be queried from the specified search engine cluster according to the target time segment in the query condition, where the queried index storage segment is the target index storage segment.

The time period corresponding to the inquired target index storage segment is intersected with the target time period, and the track recall ratio is guaranteed. For example, the target time period is 2018-08-28-00 to 2018-09-05-00, and then the corresponding target index storage segments of the time periods 2018-08-24-00 to 2018-08-30-00 and 2018-08-30-00 to 2018-09-06-00 can be inquired. The target indexes are required to be inquired from the target index storage segments respectively corresponding to the time periods of 2018-08-24-00 to 2018-08-30-00 and the time periods of 2018-08-30-00 to 2018-09-06-00.

In step S702, the index containing the target object ID in the target index storage segment is searched. Each index contains an object ID, and the index in the target index storage segment may be traversed to check whether the object ID in the traversed index is the target object ID in the query condition, and if so, the index is the index to be searched in step S702.

In step S701, the target index storage segment corresponding to the time segment where the target time segment intersects with the query condition is found, so that the indexes including the target object ID queried from the target index storage segment do not all satisfy the query condition, and further screening is required.

In step S703, for each found index, it is checked whether the maximum timestamp and/or the minimum timestamp in the index is within the target time period, and if so, the index is the target index.

In this way, the target index containing the target object ID and containing the maximum timestamp and/or the minimum timestamp within the target time period can be found from the target index storage segment. Of course, whether the specific maximum timestamp is in the target time period, whether the specific minimum timestamp is in the target time period, or whether both the specific maximum timestamp and the specific minimum timestamp need to be in the target time period may be determined as needed, and is not particularly limited.

In this embodiment, at least two index storage segments are divided in the designated search engine cluster, each index storage segment has a corresponding time period, and the index storage segments can be stored in the time periods. Therefore, when the indexes are inquired, the target index storage segment can be determined firstly, the target indexes can be found only by traversing the indexes in the target index storage segment, all indexes in the designated search engine cluster do not need to be traversed, and the index inquiry performance is improved.

The query conditions at least include: target object ID, target time period.

s704: searching an index containing a target object ID in the appointed search engine cluster;

s705: and aiming at each found index, checking whether the maximum time stamp and/or the minimum time stamp in the index is within the target time period, if so, taking the index as the target index.

In this embodiment, different from the foregoing embodiment, the designated search engine cluster does not divide the index storage segment, and directly finds out, from the designated search engine cluster, a target index that includes the target object ID and includes the maximum timestamp and/or the minimum timestamp within the target time period. The rest of the same or similar parts are not described in detail herein.

In one embodiment, the query conditions include at least: target object ID, target time period;

in step S800, obtaining a target object trajectory satisfying the query condition from the found object trajectories includes:

s801: checking whether the number of the searched object tracks is more than two;

s802: if yes, if at least two object tracks corresponding to the same target object ID exist in the searched object tracks, splicing the object tracks corresponding to the same target object ID in the searched object tracks, intercepting a first track section from the spliced object tracks, wherein time stamps in data attributes of the position information on the first track section are all in the target time period, and determining the first track section as the target track.

If more than two object tracks are found, the fact that more than two hash object tracks of the same object possibly exist is indicated, therefore, whether at least two object tracks corresponding to the same target object ID exist in the found object tracks can be checked, if yes, the object tracks corresponding to the same target object ID in the found object tracks are spliced, then a first track section is intercepted from the spliced object tracks according to a target time period in an inquiry condition, a time stamp in a data attribute of each position information on the first track section is in the target time period, the first track section is determined to be the target track, and the target track meeting the inquiry condition is obtained.

During splicing, the earlier time stamp in the data attribute of the contained position information can be ranked behind the later time stamp in the data attribute of the contained position information to realize splicing, and the continuity of each position information on the object track obtained by splicing in time is ensured.

Of course, if an object track corresponding to the same target object ID does not exist in more than two object tracks, or if only one object track is found, then splicing is not needed, a second track segment may be intercepted from the found object tracks, and the timestamps in the data attribute of each piece of location information on the second track segment are all within the target time period, and the second track segment is determined as the target track.

The present invention also provides a trajectory determination device, and in one embodiment, referring to fig. 2, the trajectory determination device 100 includes:

a data attribute obtaining module 101, configured to obtain data attributes from different acquisition devices, where the data attributes at least include: acquiring position information of equipment, an object identification ID of an object acquired by the acquisition equipment and a timestamp of the acquired object;

a data attribute storage module 102, configured to store the data attribute in a first database table;

the object track generating module 103 is configured to determine a time period T1 required for searching for data attributes when a track needs to be generated, search for data attributes with timestamps in the time period T1 from the first database table, find out data attributes containing the same object ID from the found data attributes, and sort the position information in the found data attributes according to the timestamps to obtain an object track.

In one embodiment, when the data attribute storage module stores the data attribute in the first database table, the data attribute storage module is specifically configured to:

In one embodiment, when the data attribute storage module stores the data attribute in the target partition corresponding to the time period of the timestamp in the first database table according to the timestamp in the data attribute, the data attribute storage module is specifically configured to:

In one embodiment, after the object trajectory generation module, the apparatus further comprises:

In one embodiment, the apparatus further comprises:

In one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

In one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

In one embodiment of the present invention,

the query conditions at least include: target object ID, target time period;

checking whether the number of the searched object tracks is more than two;

The implementation process of the functions and actions of each module in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units.

The invention also provides an electronic device, which comprises a processor and a memory; the memory stores a program that can be called by the processor; wherein the processor, when executing the program, implements the trajectory determination method as described in the foregoing embodiments.

The embodiment of the track determination device can be applied to electronic equipment. Taking a software implementation as an example, as a logical device, the device is formed by reading, by a processor of the electronic device where the device is located, a corresponding computer program instruction in the nonvolatile memory into the memory for operation. From a hardware aspect, as shown in fig. 5, fig. 5 is a hardware structure diagram of an electronic device where the trajectory determination apparatus 100 is located according to an exemplary embodiment of the present invention, and except for the processor 510, the memory 530, the interface 520, and the nonvolatile memory 540 shown in fig. 5, the electronic device where the apparatus 100 is located in the embodiment may also include other hardware generally according to the actual function of the electronic acquisition device, which is not described again.

The present invention also provides a machine-readable storage medium on which a program is stored, which when executed by a processor implements a trajectory determination method as described in any one of the preceding embodiments.

The present invention may take the form of a computer program product embodied on one or more storage media including, but not limited to, disk storage, CD-ROM, optical storage, and the like, having program code embodied therein. Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, and the storage of information may be accomplished by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of machine-readable 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 technologies, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by a computing device.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A trajectory determination method, comprising:

storing the data attributes in a first database table;

2. The trajectory determination method of claim 1, wherein storing the data attributes in a first database table comprises:

3. The trajectory determination method of claim 2, wherein storing the data attributes in the first database table according to a timestamp in the data attributes into a target partition corresponding to a time period in which the timestamp is located comprises:

4. The trajectory determination method according to any one of claims 1 to 3, wherein after the object trajectory is obtained by sorting the position information in the found data attributes according to the time stamps, the method further comprises:

distributing corresponding track marks for the object tracks;

5. The trajectory determination method of claim 4, further comprising:

receiving an externally input query condition;

6. The trajectory determination method of claim 5,

the query conditions at least include: target object ID, target time period;

7. The trajectory determination method of claim 5,

the query conditions at least include: target object ID, target time period;

8. The trajectory determination method of claim 5,

the query conditions at least include: target object ID, target time period;

checking whether the number of the searched object tracks is more than two;

9. A trajectory determination device, comprising:

10. The trajectory determination device of claim 9, wherein the data attribute storage module, when storing the data attributes in the first database table, is specifically configured to:

11. The trajectory determination device of claim 10, wherein, when the data attribute storage module stores the data attribute in the target partition corresponding to the time period of the timestamp in the first database table according to the timestamp in the data attribute, the data attribute storage module is specifically configured to:

12. The trajectory determination device of any one of claims 9-11, wherein after the object trajectory generation module, the device further comprises:

13. The trajectory determination device of claim 12, further comprising:

14. The trajectory determination device of claim 13,

the query conditions at least include: target object ID, target time period;

15. An electronic device comprising a processor and a memory; the memory stores a program that can be called by the processor; wherein the processor, when executing the program, implements the trajectory determination method as defined in any one of claims 1 to 8.

16. A machine-readable storage medium, having stored thereon a program which, when executed by a processor, carries out the trajectory determination method according to any one of claims 1 to 8.