Target positioning method and device, electronic equipment, storage medium and program

Abstract

The embodiment of the invention discloses a target positioning method and device, electronic equipment, a storage medium and a program. The method comprises the following steps: acquiring fuzzy semantic description data of a target location and target planar graph data of the target location; performing fuzzy matching on the fuzzy semantic description data of the target location and the target planar graph data to obtain a target fuzzy matching location pair; obtaining target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target planar graph data; wherein the target location information associated data comprises a location name, a location orientation and a local associated location coordinate of the target location; and positioning the accurate position of the target location according to the target location information association data. According to the technical scheme of the embodiment of the invention, the accuracy and efficiency of target positioning in the fuzzy positioning scene can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of positioning technology, and in particular, to a target positioning method, device, electronic device, storage medium and program. Background Art

[0002] Currently, positioning technology can accurately locate places with clear place names on the map based on natural language descriptions. However, it is impossible to accurately locate locations on the map that do not have clear place names. Summary of the invention

[0003] Embodiments of the present invention provide a target positioning method, device, electronic device, storage medium and program, which can improve the accuracy and efficiency of target positioning in fuzzy positioning scenarios.

[0004] According to one aspect of the present invention, there is provided a target positioning method, comprising:

[0005] Acquire fuzzy semantic description data of a target location and target plan view data of the target location;

[0006] Performing fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair;

[0007] Obtaining target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target plan view data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location;

[0008] The precise position of the target location is located according to the target location information associated data.

[0009] According to another aspect of the present invention, there is provided a target positioning device, comprising:

[0010] A target plan view data acquisition module, used to acquire fuzzy semantic description data of a target location and target plan view data of the target location;

[0011] A target fuzzy matching location pair acquisition module is used to perform fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair;

[0012] A location information associated data acquisition module, used to obtain target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target plan view data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location;

[0013] The location positioning module is used to locate the precise location of the target location according to the target location information associated data.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the target positioning method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target positioning method described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the target positioning method described in any embodiment of the present invention is implemented.

[0020] The embodiment of the present invention obtains fuzzy semantic description data of the target location and target floor plan data of the target location, and performs fuzzy matching on the fuzzy semantic description data of the target location and the target floor plan data to obtain a target fuzzy matching location pair. Further, target location information association data is obtained based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data, so that the precise location of the target location can be located based on the target location information association data, solving the problem of inaccurate location positioning of locations without clear location names in maps by existing positioning methods, and improving the accuracy and efficiency of target positioning in fuzzy positioning scenarios.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a target positioning method provided by Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of a target positioning method provided by Embodiment 2 of the present invention;

[0025] Figure 3 is a flowchart of a specific target positioning method provided by Embodiment 2 of the present invention;

[0026] Figure 4 is a schematic diagram of a target plane map provided by Embodiment 2 of the present invention;

[0027] Figure 5 is a schematic diagram of accurate positioning of a target location provided by Embodiment 2 of the present invention;

[0028] Figure 6 is a schematic diagram of a target positioning device provided by Embodiment 3 of the present invention;

[0029] Figure 7 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "target", "first", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1This is a flow chart of a target positioning method provided by the first embodiment of the present invention. This embodiment is applicable to the case of accurately positioning a target in a fuzzy positioning scenario. The method can be executed by a target positioning device, which can be implemented by software and / or hardware and can generally be integrated in an electronic device. The electronic device can be a terminal device or a server device. As long as it can execute the target positioning method, the embodiment of the present invention does not limit the specific device type of the electronic device. Accordingly, Figure 1 As shown, the method includes the following operations:

[0034] S110, obtaining fuzzy semantic description data of a target location and target plan view data of the target location.

[0035] Among them, the target location can be a location whose precise position is to be determined. The fuzzy semantic description data of the target location can be a semantic description of the target location that is relatively vague in the fuzzy positioning scene. The so-called fuzzy positioning scene can be understood as a scene in which the precise position of the target location is determined using relatively fuzzy semantic description data. Exemplarily, the fuzzy semantic description data may include fuzzy location coordinate data and / or location orientation associated data of the target location. The fuzzy location coordinate data may be relatively fuzzy location coordinate data described in the form of natural language. Location orientation associated data may be orientation data related to the target location. Exemplarily, the fuzzy semantic description data may be "the target location is next to the bunker near the 3rd green, with a water hazard to the west and a clubhouse building to the northeast". The target floor plan data of the target location may be two-dimensional floor plan data including the target location and its surrounding area.

[0036] In the embodiment of the present invention, in order to achieve accurate positioning of the target location, fuzzy semantic description data of the target location and target plan view data of the target location may be firstly obtained as reference data for positioning the target location.

[0037] S120, performing fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair.

[0038] The target fuzzy matching location pair may be location pair data obtained by fuzzy matching the fuzzy semantic description data of the target location and the target floor plan data.

[0039] Accordingly, after obtaining the fuzzy semantic description data of the target location and the target floor plan data of the target location, the fuzzy semantic description data of the target location and the target floor plan data can be fuzzy matched to obtain a target fuzzy matching location pair. The so-called fuzzy matching can be understood as allowing a certain error in the matching process of the fuzzy semantic description data and the target floor plan data.

[0040] S130. Obtain target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location.

[0041] The target location information associated data may be data associated with determining the precise location of the target location. The location name may be location name data used to determine the precise location of the target location in the fuzzy semantic description data. The location orientation may be the orientation of the location name relative to the target location. The local associated location coordinates may be the coordinates of the location name.

[0042] Correspondingly, after fuzzy matching the fuzzy semantic description data and the target floor plan data of the target location to obtain a target fuzzy matching location pair, target location information association data including information such as the target location's location name, location orientation, and local associated location coordinates can be obtained based on the target fuzzy matching location pair, the fuzzy semantic description data, and the target floor plan data.

[0043] S140: Locate the precise position of the target location according to the target location information associated data.

[0044] The precise location of the target location may be the precise coordinate location of the target location.

[0045] Correspondingly, after obtaining the target location information associated data, the precise position of the target location can be located according to the target location information associated data.

[0046] The above scheme matches the fuzzy semantic description data with the target floor plan data of the target location, and obtains the target location information association data based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data, thereby locating the precise position of the target location based on the target location information association data, thereby achieving accurate mapping from natural language description to map location.

[0047] The embodiment of the present invention obtains fuzzy semantic description data of the target location and target floor plan data of the target location, and performs fuzzy matching on the fuzzy semantic description data of the target location and the target floor plan data to obtain a target fuzzy matching location pair. Further, target location information association data is obtained based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data, so that the precise location of the target location can be located based on the target location information association data, solving the problem of inaccurate location positioning of locations without clear location names in maps by existing positioning methods, and improving the accuracy and efficiency of target positioning in fuzzy positioning scenarios.

[0048] Embodiment 2

[0049] Figure 2 It is a flow chart of a target positioning method provided in the second embodiment of the present invention. This embodiment is concretized based on the above embodiment. In this embodiment, a variety of specific optional implementation methods are provided for fuzzy matching of fuzzy semantic description data of the target location and target floor plan data, obtaining target location information associated data, and locating the precise position of the target location based on the target location information associated data.

[0050] Correspondingly, such as Figure 2 As shown, the method of this embodiment may include:

[0051] S210: Obtain fuzzy semantic description data of a target location and target plan view data of the target location.

[0052] S220: Parse the fuzzy semantic description data to obtain a list of associated locations and / or a list of direction words of the target location.

[0053] The associated location list of the target location may be a location list associated with the precise location of the target location in the fuzzy semantic description data, and the direction word list may be a direction word description list corresponding to the associated location list of the target location.

[0054] Figure 3 is a flowchart of a specific target positioning method provided by Embodiment 2 of the present invention. In a specific example, Figure 3 As shown, optionally, the fuzzy semantic description data can be semantically parsed by a large language model to decompose it into a list of associated locations and / or a list of directional words of the target location. Assuming that the fuzzy semantic description data is "I am northeast of AA, north of BB, and south of CC.", the result of semantically parsing the fuzzy semantic description data by a large language model can be [location (location name = "AA", location direction = "northeast"), location (location name = "BB", location direction = "north"), location (location name = "CC", location direction = "south")].

[0055] S230: Extract first location name data from the target floor plan data.

[0056] The first place name data may be all the place name data in the target floor plan data.

[0057] Correspondingly, after parsing the fuzzy semantic description data to obtain a list of associated locations and / or a list of directional words of the target location, the first location name data may be extracted from the target plan view data as reference data for fuzzy matching.

[0058] Let's continue with the above example. Figure 4is a schematic diagram of a target plane map provided by the second embodiment of the present invention. Figure 4 As shown, optionally, the first place name data in the target floor plan can be extracted through a visual language model, and the result is ["17", "AA", "BB", "CC", "DD", "EE", "FF", "GG", "HH", "II", "JJ", "KK", "MM", "NN", "XX", "YY", "21", "9", "6"].

[0059] S240: Match the associated location list of the target location with the first location name data to obtain the target fuzzy matching location pair.

[0060] Correspondingly, after obtaining the associated place list and the first place name data of the target place, the associated place list and the first place name data of the target place can be matched to obtain a target fuzzy matching place pair.

[0061] In an optional embodiment of the present invention, matching the associated place list of the target place and the first place name data to obtain the target fuzzy matching place pair may include: obtaining the matching degree between the associated place list and the first place name data; and screening the fuzzy matching place pairs whose matching degree is greater than or equal to a preset matching degree threshold as the target fuzzy matching place pairs.

[0062] The matching degree may be the similarity between the place name in the associated place list and the first place name data. The preset matching degree threshold may be a preset standard value of the matching degree. For example, the matching degree threshold may be 75%.

[0063] In an embodiment of the present invention, in order to obtain a target fuzzy matching location pair, the matching degree between the associated location list and the first location name data can be first obtained. If the matching degree is greater than or equal to a preset matching degree threshold, the corresponding fuzzy matching location pair can be filtered as a target fuzzy matching location pair; if the matching degree is less than the preset matching degree threshold, the corresponding fuzzy matching location pair can be discarded.

[0064] Continuing with the above example, the associated location list of the target location and the first location name data are matched, and the fuzzy location matching pairs with a matching degree greater than 75% are used as the target fuzzy matching location pairs. The result is: [("AA", "AA"), ("BB", "BB"), ("CC", "CC")].

[0065] S250, performing text detection and recognition on the target plan view data to obtain accurate positioning data of the target plan view.

[0066] The precise positioning data of the target plan view may be precise coordinate data corresponding to the place name in the target plan view.

[0067] Optionally, text detection and recognition may be performed on the target plan view data using optical character recognition technology to obtain pixel coordinate data corresponding to the place name in the target plan view.

[0068] S260: construct a dynamic dictionary for the precise positioning data of the target plan view.

[0069] The dynamic dictionary may be a data structure constructed based on precise positioning data of the target floor plan.

[0070] Correspondingly, after the precise positioning data of the target plan view is acquired, a dynamic dictionary may be constructed for the precise positioning data of the target plan view to store the relevant data of the target plan view.

[0071] In an optional embodiment of the present invention, constructing a dynamic dictionary for the precise positioning data of the target floor plan may include: calculating the confidence of the precise positioning data based on the precise positioning data of the target floor plan; and constructing the dynamic dictionary based on the precise positioning data, the confidence of the precise positioning data and the matching degree.

[0072] Among them, the confidence of the precise positioning data can represent the reliability of the precise positioning data.

[0073] In an embodiment of the present invention, in order to construct a dynamic dictionary for the precise positioning data of the target floor plan, the confidence of the precise positioning data can be first calculated based on the precise positioning data of the target floor plan, and further, the dynamic dictionary can be constructed based on the precise positioning data, the confidence of the precise positioning data and the matching degree.

[0074] S270: Filter the dynamic dictionary according to the target fuzzy matching location to obtain a target dynamic dictionary entry.

[0075] The target dynamic dictionary entry may be obtained by filtering the dynamic dictionary according to the target fuzzy matching location pair to obtain the dynamic dictionary entry that only includes the target fuzzy matching location pair.

[0076] Accordingly, after constructing a dynamic dictionary based on the precise positioning data of the target plan, the dynamic dictionary can be screened according to the target fuzzy matching location pairs to obtain target dynamic dictionary entries that only include the target fuzzy matching location pairs.

[0077] Continuing with the above example, a dynamic dictionary is constructed for the precise positioning data of the target floor plan, and the dynamic dictionary is screened according to the target fuzzy matching location to obtain the target dynamic dictionary entries. The results are: Location name: CC, Matching degree: 100.00%, Confidence: 0.99, Location: [[654.0,41.0], [715.0,41.0], [715.0,60.0], [654.0,60.0]]; Location name: BB, Matching degree :100.00%,confidence:0.93,location:[[675.0,549.0],[739.0,549.0],[739.0,565.0],[675.0,565.0]]; Place name:AA,match:100.00%,confidence:0.98,location:[[479.0,620.0],[546.0,620.0],[546.0,636.0],[479.0,636.0]].

[0078] S280: Combine the fuzzy semantic description data and the target dynamic dictionary entry to obtain the target location information associated data.

[0079] Correspondingly, after the dynamic dictionary is screened according to the target fuzzy matching location pairs to obtain the target dynamic dictionary entries, the fuzzy semantic description data and the target dynamic dictionary entries may be combined to obtain the target location information associated data.

[0080] Continuing with the above example, the result obtained after combining the fuzzy semantic description data and the target dynamic dictionary entry is: {"CC":(684.5,50.5), "BB":(707.0,557.0), "AA":(512.5,628.0)}; {"AA":Northeast, "BB":North, "CC":South}.

[0081] S290: Establish a linear programming model according to the target location information association data.

[0082] The linear programming model may be a model established based on target location information associated data and used to optimize a linear objective function under a set of linear inequality or equality constraints.

[0083] S2100: Solve the linear programming model to obtain the precise location of the target location.

[0084] Correspondingly, after obtaining the target location information associated data, a linear programming model can be established according to the target location information associated data, and the linear programming model can be solved to obtain the precise location of the target location according to the solution result.

[0085] Continuing with the above example, the integrated information is input into the large language model, which automatically generates the positioning code based on the predefined basic function. The linear programming model established based on the integrated information is:

[0086] x ≥ 512.500

[0087] y≤-1.000x+1151.750

[0088] y≥-1.000x+1129.250

[0089] y≤557.000

[0090] -695.750≤x≤718.250

[0091] y ≥ 50.500

[0092] The positioning code solves the linear programming model to determine the exact location of the target location. Figure 5 FIG. 1 is a schematic diagram of the accurate location of a target location provided by the second embodiment of the present invention. Figure 5 As shown, the dot marked position is the precise location of the target location.

[0093] The embodiment of the present invention parses the fuzzy semantic description data to obtain a list of associated locations and / or a list of direction words of the target location, and extracts the first location name data from the target floor plan data. Further, the list of associated locations of the target location and the first location name data can be matched to obtain a target fuzzy matching location pair. Further, text detection and recognition can be performed on the target floor plan data to obtain the precise positioning data of the target floor plan, so that a dynamic dictionary can be constructed for the precise positioning data of the target floor plan. Further, the dynamic dictionary can be screened according to the target fuzzy matching location pairs to obtain target dynamic dictionary entries, and the fuzzy semantic description data and the target dynamic dictionary entries can be combined to obtain target location information associated data, so that a linear programming model can be established according to the target location information associated data, and the linear programming model is solved to obtain the precise location of the target location, which solves the problem that the existing positioning method is inaccurate in locating locations that do not have clear location names in the map, and can improve the accuracy and efficiency of target positioning in fuzzy positioning scenarios.

[0094] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0095] It should be noted that any arrangement and combination of the technical features in the above embodiments also falls within the protection scope of the present invention.

[0096] Embodiment 3

[0097] Figure 6 is a schematic diagram of a target positioning device provided by Embodiment 3 of the present invention, such as Figure 6 As shown, the device includes: a target plan view data acquisition module 310, a target fuzzy matching location pair acquisition module 320, a location information associated data acquisition module 330 and a position positioning module 340, wherein:

[0098] The target floor plan data acquisition module 310 is used to acquire fuzzy semantic description data of a target location and target floor plan data of the target location.

[0099] The target fuzzy matching location pair acquisition module 320 is used to perform fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair.

[0100] The location information associated data acquisition module 330 is used to obtain target location information associated data based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location.

[0101] The location positioning module 340 is used to locate the precise location of the target location according to the target location information associated data.

[0102] The embodiment of the present invention obtains fuzzy semantic description data of the target location and target floor plan data of the target location, and performs fuzzy matching on the fuzzy semantic description data of the target location and the target floor plan data to obtain a target fuzzy matching location pair. Further, target location information association data is obtained based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data, so that the precise location of the target location can be located based on the target location information association data, solving the problem of inaccurate location positioning of locations without clear location names in maps by existing positioning methods, and improving the accuracy and efficiency of target positioning in fuzzy positioning scenarios.

[0103] Optionally, the fuzzy semantic description data includes fuzzy location coordinate data and / or location orientation association data of the target location.

[0104] Optionally, the target fuzzy matching location pair acquisition module 320 is specifically used to parse the fuzzy semantic description data to obtain a list of associated locations and / or a list of direction words of the target location; extract the first location name data from the target floor plan data; match the list of associated locations of the target location with the first location name data to obtain the target fuzzy matching location pair.

[0105] Optionally, the target fuzzy matching place pair acquisition module 320 is further used to obtain the matching degree between the associated place list and the first place name data; and filter the fuzzy matching place pairs with a matching degree greater than or equal to a preset matching degree threshold as target fuzzy matching place pairs.

[0106] Optionally, the location information associated data acquisition module 330 is specifically used to perform text detection and recognition on the target floor plan data to obtain the precise positioning data of the target floor plan; construct a dynamic dictionary for the precise positioning data of the target floor plan; filter the dynamic dictionary according to the target fuzzy matching location pairs to obtain target dynamic dictionary entries; combine the fuzzy semantic description data and the target dynamic dictionary entries to obtain the target location information associated data.

[0107] Optionally, the location information associated data acquisition module 330 is further used to calculate the confidence of the precise positioning data based on the precise positioning data of the target plan view; and construct the dynamic dictionary based on the precise positioning data, the confidence of the precise positioning data and the matching degree.

[0108] Optionally, the position positioning module 340 is specifically used to establish a linear programming model according to the target location information associated data; solve the linear programming model to obtain the precise location of the target location.

[0109] The above-mentioned target positioning device can execute the target positioning method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the target positioning method provided by any embodiment of the present invention.

[0110] Since the target positioning device introduced above is a device that can execute the target positioning method in the embodiment of the present invention, based on the target positioning method introduced in the embodiment of the present invention, the technical personnel in the field can understand the specific implementation of the target positioning device of the present embodiment and its various variations, so how the target positioning device implements the target positioning method in the embodiment of the present invention is not described in detail here. As long as the technical personnel in the field implement the device used by the target positioning method in the embodiment of the present invention, it belongs to the scope of protection of this application.

[0111] Embodiment 4

[0112] Figure 7A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0113] like Figure 7 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0115] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a target positioning method.

[0116] In some embodiments, the target positioning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the target positioning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the target positioning method in any other appropriate manner (e.g., by means of firmware).

[0117] Optionally, a target positioning method may include: obtaining fuzzy semantic description data of a target location and target floor plan data of the target location; performing fuzzy matching on the fuzzy semantic description data and the target floor plan data of the target location to obtain a target fuzzy matching location pair; obtaining target location information association data based on the target fuzzy matching location pair, the fuzzy semantic description data and the target floor plan data; wherein the target location information association data includes the location name, location orientation and local associated location coordinates of the target location; and locating the precise position of the target location based on the target location information association data.

[0118] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0120] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0122] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0123] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0124] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0125] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A target positioning method, characterized in that: include: Acquire fuzzy semantic description data of a target location and target plan view data of the target location; Performing fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair; Obtaining target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target plan view data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location; The precise position of the target location is located according to the target location information associated data.

2. The method according to claim 1, characterized in that The fuzzy semantic description data includes fuzzy location coordinate data and / or location orientation associated data of the target location; The fuzzy matching of the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair includes: Parsing the fuzzy semantic description data to obtain a list of associated locations and / or a list of directional words of the target location; extracting first location name data from the target plan view data; The associated location list of the target location and the first location name data are matched to obtain the target fuzzy matching location pair.

3. The method according to claim 2, characterized in that The matching of the associated location list of the target location and the first location name data to obtain the target fuzzy matching location pair includes: Obtaining a matching degree between the associated location list and the first location name data; The fuzzy matching location pairs whose matching degree is greater than or equal to the preset matching degree threshold are filtered as target fuzzy matching location pairs.

4. The method according to claim 1, characterized in that: The step of obtaining target location information association data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target plan view data includes: Performing text detection and recognition on the target plan data to obtain accurate positioning data of the target plan; constructing a dynamic dictionary for the precise positioning data of the target plan; Filtering the dynamic dictionary according to the target fuzzy matching location to obtain a target dynamic dictionary entry; The fuzzy semantic description data and the target dynamic dictionary entry are combined to obtain the target location information associated data.

5. The method according to claim 4, characterized in that The method of constructing a dynamic dictionary for the precise positioning data of the target plan view includes: Calculating the confidence of the precise positioning data according to the precise positioning data of the target plane map; The dynamic dictionary is constructed according to the precise positioning data, the confidence level and the matching level of the precise positioning data.

6. The method according to claim 1, characterized in that The step of locating the precise position of the target location according to the target location information associated data includes: Establishing a linear programming model based on the target location information associated data; The linear programming model is solved to obtain the precise location of the target location.

7. A target positioning device, characterized in that: include: A target plan view data acquisition module, used to acquire fuzzy semantic description data of a target location and target plan view data of the target location; A target fuzzy matching location pair acquisition module is used to perform fuzzy matching on the fuzzy semantic description data of the target location and the target plan view data to obtain a target fuzzy matching location pair; A location information associated data acquisition module, used to obtain target location information associated data according to the target fuzzy matching location pair, the fuzzy semantic description data and the target plan view data; wherein the target location information associated data includes the location name, location orientation and local associated location coordinates of the target location; The location positioning module is used to locate the precise location of the target location according to the target location information associated data.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the target positioning method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target positioning method described in any one of claims 1-6 when executed.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the target positioning method according to any one of claims 1 to 6 is implemented.

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