Cable line optimization method, device and equipment based on artificial intelligence reasoning model

Abstract

The embodiment of the invention discloses a cable line optimization method, device and equipment based on an artificial intelligence reasoning model. A specific embodiment of the method comprises the following steps: acquiring each candidate cable passing point identifier; obtaining feature information of each geographic space; performing line optimization data extraction processing on each piece of geographic space feature information; obtaining cable approach point identification directed graph data; traversing a cable approach point identification digraph corresponding to the cable approach point identification digraph data; performing optimization processing on the initial cable line information to obtain optimized cable line information corresponding to the preset cable terminal field identifier group; generating cable line laying information; generating cable laying mode information corresponding to the optimized cable line information; and determining the optimized cable line information and the cable laying mode information as cable line information, and displaying the cable line information on a preset cable line optimization page. According to the embodiment, the experience feeling of a cable construction user is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and more particularly to a cable line optimization method, apparatus, and device based on an artificial intelligence reasoning model. Background Art

[0002] With the development of society and industry, the increasing demand for electricity has made the design and planning of cable lines particularly important. Cable line optimization based on artificial intelligence reasoning models is a technology for generating cable line information. Currently, when generating cable line information, the common method is to directly use the identifiers of each point along the shortest distance path between the starting point and the end point of the cable terminal field as the cable line information.

[0003] However, when using the above method to generate cable line information, the following technical problems often occur:

[0004] Directly using the identifiers of each point along the shortest path between the starting point and the end point of the cable terminal field as cable route information fails to consider the impact of geospatial characteristics on the cable route. The shortest path may traverse complex terrain or obstacles, increasing construction difficulty and risk, resulting in poor usability of cable route information. Furthermore, directly using the identifiers of each point along the shortest path between the starting point and the end point of the cable terminal field as cable route information, without generating a cable laying method tailored to the geospatial characteristics of each point along the path, results in a poor user experience for cable construction users.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure provide a method, apparatus, electronic device, and computer-readable medium for generating cable line information to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In the first aspect, some embodiments of the present disclosure provide a cable line optimization method based on an artificial intelligence reasoning model, the method comprising: obtaining each candidate cable waypoint identifier corresponding to a preset cable terminal field identifier group, wherein the above-mentioned preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field ending point identifier; obtaining each geographic spatial feature information corresponding to the above-mentioned each candidate cable waypoint identifier from a preset database, wherein each geographic spatial feature information in the above-mentioned each geographic spatial feature information includes terrain elevation information of the waypoint, underground pipeline information of the waypoint, soil condition information of the waypoint, house distribution map of the waypoint, and pollution area raster data; performing line optimization data extraction processing on the above-mentioned each geographic spatial feature information to obtain a line optimization extraction data set corresponding to the above-mentioned each candidate cable waypoint identifier; obtaining the above-mentioned preset cable terminal field identifier group and the above-mentioned each geographic spatial feature information from the preset database. The pre-constructed cable waypoint identification directed graph data of each candidate cable waypoint identification; traversing the cable waypoint identification directed graph corresponding to the above cable waypoint identification directed graph data to obtain each initial cable line information corresponding to the above preset cable terminal field identification group; based on the above line optimization extraction data set and the pre-trained artificial intelligence reasoning model, optimizing the above each initial cable line information to obtain the optimized cable line information corresponding to the above preset cable terminal field identification group; based on the above each geographic space feature information, generating the cable line laying information corresponding to the above optimized cable line information; based on the above cable line laying information, generating the cable laying method information corresponding to the above optimized cable line information; determining the above optimized cable line information and the above cable laying method information as cable line information, and displaying the above cable line information on the preset cable line optimization page.

[0009] In a second aspect, some embodiments of the present disclosure provide a cable line optimization device based on an artificial intelligence reasoning model, the device comprising: a first acquisition unit, configured to acquire each candidate cable waypoint identifier corresponding to a preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field ending point identifier; a second acquisition unit, configured to acquire each geographic spatial feature information corresponding to each candidate cable waypoint identifier from a preset database, wherein each of the above-mentioned geographic spatial feature information includes terrain elevation information of the waypoint, underground pipeline information of the waypoint, soil condition information of the waypoint, house distribution map of the waypoint, and pollution area raster data; an extraction and processing unit, configured to perform line optimization data extraction processing on each of the above-mentioned geographic spatial feature information to obtain a line optimization extraction data set corresponding to each of the above-mentioned candidate cable waypoint identifiers; a third acquisition unit, configured to acquire each candidate cable waypoint identifier corresponding to the above-mentioned preset cable terminal field identifier group and the above-mentioned candidate cable waypoint identifier from the preset database. Pre-constructed cable waypoint identification directed graph data of cable waypoint identification is selected; a traversal unit is configured to traverse the cable waypoint identification directed graph corresponding to the above cable waypoint identification directed graph data, and obtain each initial cable line information corresponding to the above preset cable terminal field identification group; an optimization processing unit is configured to optimize the above each initial cable line information based on the above line optimization extraction data set and a pre-trained artificial intelligence inference model, and obtain the optimized cable line information corresponding to the above preset cable terminal field identification group; a first generation unit is configured to generate cable line laying information corresponding to the above optimized cable line information based on the above each geographic spatial feature information; a second generation unit is configured to generate cable laying method information corresponding to the above optimized cable line information based on the above cable line laying information; a display unit is configured to determine the above optimized cable line information and the above cable laying method information as cable line information, and display the above cable line information on the preset cable line optimization page.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0012] The above-mentioned embodiments of the present disclosure have the following beneficial effects: Through the cable route optimization method based on artificial intelligence reasoning models in some embodiments of the present disclosure, the availability of cable route information and the user experience of cable construction are improved. Specifically, the reason for the poor availability of cable route information and the user experience of cable construction is that the identifiers of each waypoint along the shortest distance path between the cable terminal field starting point and the cable terminal field terminal end point are directly used as cable route information, without considering the impact of geospatial feature information on the cable route. The shortest path may pass through complex terrain or obstacles, increasing the difficulty and risk of construction, resulting in poor availability of cable route information. At the same time, the identifiers of each waypoint along the shortest distance path between the cable terminal field starting point and the cable terminal field terminal end point are directly used as cable route information, without generating a cable laying method for the cable route based on the geospatial feature information of each waypoint, resulting in a poor user experience for cable construction. Based on this, the cable route optimization method based on artificial intelligence reasoning models in some embodiments of the present disclosure first obtains each candidate cable waypoint identifier corresponding to a preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field terminal identifier. Thus, candidate cable waypoint identifiers for the cable terminal field starting point, the cable terminal field terminal end point, and each waypoint between the two points can be obtained. Subsequently, geospatial feature information corresponding to each candidate cable waypoint identifier is retrieved from a preset database. Each of these geospatial feature information includes terrain elevation information at the waypoint, underground pipeline information at the waypoint, soil condition information at the waypoint, a housing distribution map at the waypoint, and polluted area raster data. Thus, geospatial feature information for the cable terminal field starting point, the cable terminal field terminal end point, and each waypoint between the two points can be obtained. Then, route optimization data extraction is performed on each of these geospatial feature information to obtain route optimization extraction datasets corresponding to each candidate cable waypoint identifier. Data extraction is performed on each of these geospatial feature information to obtain route optimization extraction datasets that influence the cable line. Next, pre-constructed cable waypoint identifier directed graph data corresponding to the preset cable terminal field identifier group and each candidate cable waypoint identifier is retrieved from the preset database. Thus, cable waypoint identifier directed graph data representing the connection relationship between the preset cable terminal field identifier group and each of the candidate cable waypoint identifiers can be obtained. Subsequently, the cable waypoint identifier directed graph corresponding to the cable waypoint identifier directed graph data is traversed to obtain the initial cable route information corresponding to the preset cable terminal field identifier group. Thus, initial cable route information representing all route paths between the cable terminal field starting point and the cable terminal field ending point can be obtained.Subsequently, based on the aforementioned route optimization extraction dataset and a pre-trained artificial intelligence inference model, each initial cable route information is optimized to obtain optimized cable route information corresponding to the preset cable terminal field identification group. Thus, using the route optimization extraction dataset and a pre-trained artificial intelligence inference model that affect the cable route, each initial cable route information is optimized to obtain optimized cable route information with higher usability. Subsequently, based on the aforementioned geographic spatial feature information, cable route laying information corresponding to the optimized cable route information is generated. Thus, cable route laying information for generating cable laying method information is obtained. Next, based on the aforementioned cable route laying information, cable laying method information corresponding to the optimized cable route information is generated. Thus, cable laying method information suitable for the optimized cable route information is generated. The optimized cable route information and the cable laying method information are determined as cable route information, and the cable route information is displayed on a preset cable route optimization page. Thus, the cable route information, including the optimized cable route information and the cable laying method information, can be displayed on the preset cable route optimization page, improving the user experience for cable construction users. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of a cable route optimization method based on an artificial intelligence reasoning model according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of a cable line optimization device based on an artificial intelligence reasoning model according to the present disclosure;

[0016] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Figure 1 The process 100 of some embodiments of the cable line optimization method based on artificial intelligence reasoning model according to the present disclosure is shown. The cable line optimization method based on artificial intelligence reasoning model includes the following steps:

[0024] Step 101: Obtain each candidate cable passing point identifier corresponding to a preset cable terminal field identifier group.

[0025] In some embodiments, the execution subject (e.g., a computing device) of the cable line optimization method based on the artificial intelligence reasoning model can obtain each candidate cable waypoint identifier of the corresponding preset cable terminal field identifier group through a wired connection method or a wireless connection method. The above-mentioned preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field end point identifier. In practice, the above-mentioned execution subject can obtain each candidate cable waypoint identifier of the corresponding preset cable terminal field identifier group from a preset database (e.g., a relational database) or a preset power management system. The above-mentioned cable terminal field starting point identifier can be the identifier of the cable terminal station at the starting point of the cable line (e.g., the name of the cable terminal station). The above-mentioned cable terminal field end point identifier can be the identifier of the cable terminal station at the end point of the cable line. The above-mentioned candidate cable waypoint identifiers can be the identifiers of each possible route between the cable terminal station at the starting point of the cable line and the cable terminal station at the end point of the cable line.

[0026] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0027] Step 102: Acquire geographic space feature information corresponding to each candidate cable route point identifier from a preset database.

[0028] In some embodiments, the execution entity may obtain the various geospatial feature information corresponding to the candidate cable waypoint identifiers from a preset database. Each of the geospatial feature information includes terrain elevation information at the waypoint, underground pipeline information at the waypoint, soil condition information at the waypoint, a house distribution map at the waypoint, and polluted area raster data. The preset database may be a geographic information system (GIS) database. Each of the candidate cable waypoint identifiers corresponds to each of the geospatial feature information. The terrain elevation information at the waypoint may indicate the terrain elevation of the waypoint. For example, the terrain elevation information at the waypoint may be the average elevation value of the terrain at the waypoint, and the terrain elevation information at the waypoint may be "average elevation value: 15m." The underground pipeline information at the waypoint may indicate underground pipeline data indicating the buried pipeline conditions at the waypoint. The underground pipeline information at the waypoint may be text information. For example, the underground pipeline information at the waypoints may be "underground pipeline data: one water supply pipe with a diameter of 300 mm and a burial depth of 1.5 m; one gas pipeline with a diameter of 100 mm and a burial depth of 1.2 m." The soil condition information at the waypoints may indicate the soil conditions at the waypoints. The soil condition information at the waypoints may be text information. For example, the soil condition information at the waypoints may be "high aggregate content, suitable for building foundations." The house distribution map at the waypoints may be a bird's-eye view of the waypoints. The polluted area raster data may indicate the degree of pollution or pollution sources at different locations at the waypoints. For example, the above-mentioned polluted area raster data can be "pixel position is (1, 1), pollution concentration: 30 (mg / L), pollution source: chemical plant wastewater; pixel position is (2, 1), pollution concentration: pollution concentration: 15 (mg / L), pollution source: agricultural runoff; pixel position is (1, 2), pollution concentration: pollution concentration: 0, pollution source: no pollution; pixel position is (2, 2), pollution concentration: pollution concentration: 35 (mg / L), pollution source: chemical plant wastewater".

[0029] Step 103 : performing line optimization data extraction processing on each geographic space feature information to obtain a line optimization extraction data set corresponding to each candidate cable waypoint identifier.

[0030] In some embodiments, the execution entity may perform line optimization data extraction processing on each of the geographic spatial feature information to obtain a line optimization extraction data set corresponding to each of the candidate cable passing point identifiers.

[0031] In some optional implementations of some embodiments, the execution entity may perform route optimization data extraction processing on the various geographic spatial feature information to obtain a route optimization extraction data set corresponding to each candidate cable waypoint identifier through the following steps:

[0032] In the first step, for each of the above-mentioned geographic spatial feature information, the following extraction steps are performed:

[0033] The first sub-step is to determine the terrain elevation information of the waypoints included in the above-mentioned geographic spatial feature information as the terrain elevation information of the target waypoints.

[0034] The second sub-step is to determine the underground pipeline information of the waypoint included in the above-mentioned geographic space feature information as the underground pipeline information of the target waypoint.

[0035] The third sub-step is to determine the house distribution map of the waypoints included in the above-mentioned geographic space feature information as the house distribution map to be extracted.

[0036] The fourth sub-step involves performing identification and extraction processing on the house distribution map to be extracted to obtain the number of houses distributed at the waypoints. In practice, this can be accomplished by using a pre-trained house number recognition model to identify the number of houses in the house distribution map to be extracted as the number of houses distributed at the waypoints. The pre-trained house number recognition model can be a YOLO model that takes the house distribution map to be extracted as input and outputs the number of houses. Optionally, the execution entity can also use template matching technology to identify the number of houses in the house distribution map to be extracted.

[0037] The fifth sub-step is to determine the terrain elevation information of the target waypoint, the underground pipeline information of the target waypoint and the number of houses distributed at the waypoint as route optimization extraction data corresponding to the geographic space feature information.

[0038] The second step is to determine the determined line optimization extraction data into a line optimization extraction data set, wherein each line optimization extraction data in the line optimization extraction data set corresponds to each candidate cable waypoint identifier in the candidate cable waypoint identifiers.

[0039] Step 104: obtaining pre-built cable waypoint identification directed graph data corresponding to the preset cable terminal field identification group and each candidate cable waypoint identification from a preset database.

[0040] In some embodiments, the execution entity may obtain pre-constructed cable waypoint identification directed graph data corresponding to the preset cable terminal field identification group and each candidate cable waypoint identification from the preset database. The cable waypoint identification directed graph data may represent a cable waypoint identification directed graph. The identifications of each node in the cable waypoint identification directed graph include each candidate cable waypoint identification, a cable terminal field starting point identification, and a cable terminal field end point identification. The cable waypoint identification directed graph may represent a directed graph of the connection relationships between each node corresponding to each candidate cable waypoint identification, cable terminal field starting point identification, and cable terminal field end point identification. For example, the cable terminal field starting point identification and the cable terminal field end point identification may be represented by identification A and identification D, respectively, and each candidate cable waypoint identification may be represented by identification C and identification B. The cable waypoint identification directed graph data may be represented as "'Identification A': ['Identification B', 'Identification C'], 'Identification B': ['Identification D'], 'Identification C': ['Identification D']."

[0041] Step 105 , traverse the cable route point identification directed graph corresponding to the cable route point identification directed graph data to obtain each initial cable line information corresponding to the preset cable terminal field identification group.

[0042] In some embodiments, the execution subject may traverse the cable waypoint identification directed graph corresponding to the cable waypoint identification directed graph data to obtain each initial cable line information corresponding to the preset cable terminal field identification group. Each of the initial cable line information includes a cable waypoint identification sequence, the cable waypoint identification sequence includes each cable waypoint identification, and the cable waypoint identification includes each candidate cable waypoint identification, the cable terminal field starting point identification, and the cable terminal field end point identification. In practice, the execution subject may traverse the cable waypoint identification directed graph corresponding to the cable waypoint identification directed graph data through a depth-first search (DFS) algorithm to obtain each initial cable line information corresponding to the preset cable terminal field identification group.

[0043] In some optional implementations of some embodiments, the execution entity may traverse the cable path point identification directed graph corresponding to the cable path point identification directed graph data through the following steps to obtain each initial cable line information corresponding to the preset cable terminal field identification group:

[0044] The first step is to add the cable terminal field starting point identifier in the above-mentioned cable path point identifier directed graph to the first preset sequence.

[0045] In the second step, the node identifiers corresponding to the respective direct successor nodes of the node corresponding to the cable terminal field starting point identifier in the cable waypoint identifier directed graph are determined as target candidate cable waypoint identifiers. Each of the direct successor nodes can be a node that is directly reachable from the node corresponding to the cable terminal field starting point identifier via a unidirectional edge.

[0046] In the third step, based on the identifiers of each target candidate cable waypoint and the first preset sequence, the following determination steps are performed:

[0047] In the first sub-step, for each target candidate cable waypoint identifier in each target candidate cable waypoint identifier, perform the following steps:

[0048] Sub-step one: in response to determining that the target candidate cable waypoint identifier is the cable terminal field end identifier, adding the target candidate cable waypoint identifier to a first preset sequence to obtain a target first preset sequence.

[0049] Sub-step 2: In response to determining that the target candidate cable waypoint identifier is not a cable terminal field end identifier, perform the following steps:

[0050] The first sub-step is to add the target candidate cable waypoint identifier to the first preset sequence to update the first preset sequence.

[0051] In the second sub-step, in response to determining that each candidate cable waypoint identification corresponding to each direct successor node of the node corresponding to the target candidate cable waypoint identification in the cable waypoint identification directed graph is not empty, each candidate cable waypoint identification corresponding to each direct successor node of the node corresponding to the target candidate cable waypoint identification in the cable waypoint identification directed graph is determined as each target candidate cable waypoint identification to update each target candidate cable waypoint identification.

[0052] The third sub-step is to execute the above-mentioned determination step again according to the updated identifiers of each target candidate cable waypoint and the updated first preset sequence.

[0053] The second sub-step is to determine the determined first preset sequences of each target as each initial cable line information corresponding to the above-mentioned preset cable terminal field identification group. Among them, each of the above-mentioned initial cable line information includes a cable waypoint identification sequence, the above-mentioned cable waypoint identification sequence includes each cable waypoint identification, and the above-mentioned each cable waypoint identification includes each candidate cable waypoint identification, a cable terminal field starting point identification and a cable terminal field end point identification. As an example, the above-mentioned cable waypoint identification directed graph data can be expressed as "'Identification A': ['Identification B', 'Identification C'], 'Identification B': ['Identification D'], 'Identification C': ['Identification D']". The above-mentioned initial cable line information can be "{Identification A, Identification B, Identification D}, {Identification A, Identification C, Identification D}".

[0054] Step 106 , based on the line optimization extraction data set and the pre-trained artificial intelligence reasoning model, optimize the initial cable line information to obtain the optimized cable line information corresponding to the preset cable terminal field identification group.

[0055] In some embodiments, the above-mentioned execution entity can optimize the above-mentioned initial cable line information based on the above-mentioned line optimization extraction data set and the pre-trained artificial intelligence reasoning model to obtain the optimized cable line information corresponding to the above-mentioned preset cable terminal field identification group.

[0056] Before generating cable line information, it is necessary to optimize the above-mentioned initial cable line information based on the line optimization extraction data set and the pre-trained artificial intelligence reasoning model to obtain the optimized cable line information corresponding to the above-mentioned preset cable terminal field identification group. Although the above-mentioned cable line information is optimized based on the line optimization extraction data set, the impact of geographic spatial feature information on the cable line is taken into account, but the total distance of the line is not taken into account. When the total distance of the cable line corresponding to the optimized cable line information obtained after optimization is too long, it may lead to an increase in cable power transmission loss in the later stage. The feasibility of the cable line corresponding to the optimized cable line information obtained after optimization is still poor, and it is necessary to re-optimize the initial cable line information, which wastes computer computing resources.

[0057] Faced with the above technical problems, the inventors decided to adopt the following solutions:

[0058] In some optional implementations of some embodiments, the execution entity may optimize the initial cable line information based on the line optimization extraction data set and the pre-trained artificial intelligence reasoning model through the following steps to obtain optimized cable line information corresponding to the preset cable terminal field identification group:

[0059] In the first step, for each piece of initial cable line information, perform the following steps:

[0060] The first sub-step is to determine the cable waypoint identification sequence included in the initial cable line information as a reference cable waypoint identification sequence.

[0061] The second sub-step is to determine the distance between the waypoints corresponding to each two adjacent reference cable waypoint identifiers in the above-mentioned reference cable waypoint identifier sequence. In practice, the above-mentioned execution entity can call a preset map API interface to determine the distance between the waypoints corresponding to each two adjacent reference cable waypoint identifiers in the above-mentioned reference cable waypoint identifier sequence. The above-mentioned preset map API interface can be a preset map application interface. As an example, the above-mentioned reference cable waypoint identifier sequence can be {identifier A, identifier C, identifier D}, and the distance between the waypoints corresponding to each two adjacent reference cable waypoint identifiers can be the distance between the waypoints corresponding to identifier A and identifier C, and the distance between the waypoints corresponding to identifier C and identifier D.

[0062] In the third sub-step, the sum of the determined distances is determined as the total distance information corresponding to the initial cable line information.

[0063] The fourth sub-step is to determine each line optimization extraction data corresponding to each candidate cable waypoint identifier in the reference cable waypoint identifier sequence in the line optimization extraction data set as a reference line optimization extraction data set corresponding to the initial cable line information.

[0064] The fifth sub-step is to add the total distance information as reference line optimization extraction data to the reference line optimization extraction data set to update the reference line optimization extraction data set and obtain an updated reference line optimization extraction data set corresponding to the initial cable line information.

[0065] In the second step, each updated reference line optimization extraction data set corresponding to each of the above-mentioned initial cable line information is input into the conversion layer of a pre-trained artificial intelligence reasoning model to obtain each optimized feature extraction information set corresponding to each of the above-mentioned updated reference line optimization extraction data sets. The above-mentioned artificial intelligence reasoning model includes the above-mentioned conversion layer, feature fusion layer, fitness prediction layer, and output layer. The above-mentioned conversion layer can be an embedding layer (Embedding Layer) that takes each updated reference line optimization extraction data set as input data and each optimized feature extraction information set as output data. Each optimized feature extraction information in each of the above-mentioned optimized feature extraction information sets can be a feature vector that represents the optimized feature extraction information.

[0066] In the third step, for each of the above-mentioned optimized feature extraction information sets, the above-mentioned optimized feature extraction information set is input into the above-mentioned feature fusion layer to obtain the fused feature information corresponding to the above-mentioned optimized feature extraction information set. The above-mentioned feature fusion layer can be a concatenation layer (Concatenate Layer) that uses the optimized feature extraction information set as input data and the fused feature information as output data. The above-mentioned fused feature information can be a feature vector obtained by concatenating the feature vectors representing the various optimized feature extraction information sets.

[0067] In the fourth step, the obtained fusion feature information is determined as target fusion feature information, wherein each target fusion feature information in the target fusion feature information corresponds to each initial cable line information in the initial cable line information.

[0068] In the fifth step, the above-mentioned target fusion feature information is input into the above-mentioned fitness prediction layer to obtain the fitness scores corresponding to the above-mentioned target fusion feature information. The above-mentioned fitness prediction layer can be a fully connected layer. Each of the above-mentioned fitness scores can represent the comprehensive performance or suitability of the cable line corresponding to the corresponding initial cable line information under the updated reference line optimization extraction dataset. The above-mentioned fitness score can be the sum of the scores corresponding to each updated reference line optimization extraction data in the above-mentioned updated reference line optimization extraction dataset. The above-mentioned fitness prediction layer can have a preset correspondence table. As an example, the above-mentioned initial cable line information can be "{identifier A, identifier B, identifier D}". The updated reference line optimization extraction dataset corresponding to the above-mentioned {identifier A, identifier B, identifier D} can be "terrain elevation information of the waypoint: average elevation value: 15m, underground pipeline information of the waypoint: no underground pipeline, number of houses distributed at the waypoint: 0". With the above-mentioned "terrain elevation information of the passing point: average elevation value: 15m, underground pipeline information of the passing point: no underground pipeline, number of houses distributed at the passing point: 0, total distance information: 10km". The above-mentioned correspondence table can be "the average elevation value included in the terrain elevation information of the passing point is within the range of less than or equal to 15m, and the corresponding score can be 0 if the average elevation value included in the terrain elevation information of the passing point is within the range of greater than 15m. The score corresponding to the underground pipeline information of the passing point indicating that there are underground pipelines and the number of underground pipelines is greater than the preset value can be 0, the score corresponding to the underground pipeline information of the passing point indicating that there are underground pipelines and the number of underground pipelines is less than or equal to the preset value can be 1, and the score corresponding to the underground pipeline information of the passing point indicating that there are no underground pipelines can be 2. The score corresponding to the number of houses distributed at the passing point is less than or equal to the preset number (for example, 30) can be 2, and the score corresponding to the passing point is less than or equal to the preset number (for example, 30). If the number of houses at a point is greater than a preset number, the score corresponding to the score can be 0. If the total distance information contains a distance greater than 50 km, the score corresponding to the score can be 0. If the total distance information contains a distance less than or equal to 50 km, the score corresponding to the score can be 2. Therefore, the above-mentioned "Terrain elevation information at waypoints: average elevation value: 15m, underground pipeline information at waypoints: no underground pipelines, number of houses at waypoints: 0" can have a fitness score (the sum of the scores corresponding to the optimized extracted data of each updated reference route) corresponding to the above-mentioned "Terrain elevation information at waypoints: average elevation value: 15m, underground pipeline information at waypoints: no underground pipelines, number of houses at waypoints: 0, total distance information: 10 km" can be 8.

[0069] In the sixth step, the above-mentioned fitness scores and the above-mentioned initial cable line information are input into the above-mentioned output layer to obtain the optimized cable line information. Among them, the above-mentioned output layer can determine the fitness score that meets the preset conditions (for example, the maximum fitness score) among the fitness scores as the target fitness score, and then output the initial cable line information corresponding to the above-mentioned target fitness score as the optimized cable line information. The above-mentioned artificial intelligence reasoning model can be a neural network model (for example, a recurrent neural network (RNN) and its variants (such as long short-term memory networks (LSTM)) that takes the initial cable line information and the updated reference line optimization extraction data sets corresponding to the above-mentioned initial cable line information as input and outputs the optimized cable line information.

[0070] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the technical problem that "the feasibility of the cable line corresponding to the optimized cable line information is still poor, and re-optimization causes a waste of computer computing resources". The factors that lead to the poor feasibility of the cable line corresponding to the optimized cable line information and the waste of computer computing resources are often as follows: before generating the cable line information, it is necessary to extract the data set according to the line optimization, optimize the above-mentioned initial cable line information, and obtain the optimized cable line information corresponding to the above-mentioned preset cable terminal field identification group. Although the above-mentioned cable line information is optimized according to the line optimization extraction data set, the influence of geographic spatial feature information on the cable line is taken into account, but the total distance of the line is not taken into account. When the total distance of the cable line corresponding to the optimized cable line information obtained after optimization is too long, it may cause an increase in the cable power transmission loss in the later stage. The feasibility of the cable line corresponding to the optimized cable line information obtained after optimization is still poor, and it is necessary to re-optimize the initial cable line information, which wastes computer computing resources. If the above factors are solved, the effect of improving the feasibility of the cable line corresponding to the optimized cable line information and reducing the waste of computer computing resources can be achieved. In order to achieve this effect, first, for each of the above-mentioned initial cable line information, the following steps are performed: First, the cable waypoint identification sequence included in the above-mentioned initial cable line information is determined as the reference cable waypoint identification sequence. In this way, a reference cable waypoint identification sequence for determining the distances between the waypoints corresponding to the initial cable line information can be obtained. Second, the distance between the waypoints corresponding to each two adjacent reference cable waypoint identifications in the above-mentioned reference cable waypoint identification sequence is determined. In this way, the distances between the waypoints corresponding to the initial cable line information can be obtained. Third, the sum of the determined distances is determined as the total distance information corresponding to the above-mentioned initial cable line information. Fourth, the line optimization extraction data corresponding to each candidate cable waypoint identification in the above-mentioned reference cable waypoint identification sequence in the above-mentioned line optimization extraction data set is determined as the reference line optimization extraction data set corresponding to the above-mentioned initial cable line information. In this way, a reference line optimization extraction data set for generating an updated reference line optimization extraction data set can be obtained. In the fifth step, the total distance information is added as reference route optimization extraction data to the reference route optimization extraction dataset to update the reference route optimization extraction dataset, thereby obtaining an updated reference route optimization extraction dataset corresponding to the initial cable route information. Thus, the total distance information can be added to the reference route optimization extraction dataset. Next, each updated reference route optimization extraction dataset corresponding to each initial cable route information is input into the conversion layer of a pre-trained artificial intelligence inference model to obtain an optimized feature extraction information set corresponding to each updated reference route optimization extraction dataset.The artificial intelligence inference model includes the conversion layer, feature fusion layer, fitness prediction layer, and output layer. Thus, each updated reference line optimized extraction dataset can be converted into an optimized feature extraction information set. Subsequently, for each optimized feature extraction information set in each optimized feature extraction information set, the optimized feature extraction information set is input into the feature fusion layer to obtain fused feature information corresponding to the optimized feature extraction information set. Thus, the individual optimized feature extraction information in the converted optimized feature extraction information set can be fused to obtain fused feature information for the corresponding optimized feature extraction information set. Subsequently, each obtained fused feature information is determined as a target fused feature information, wherein each target fused feature information in each target fused feature information corresponds one-to-one to each initial cable line information in each initial cable line information. Subsequently, each target fused feature information is input into the fitness prediction layer to obtain a fitness score corresponding to each target fused feature information. Thus, a fitness score can be obtained, representing the suitability of the cable line corresponding to each initial cable line information under the corresponding updated reference line optimized extraction dataset. Then, the above-mentioned fitness scores and the above-mentioned initial cable line information are input into the above-mentioned output layer to obtain optimized cable line information. In this way, optimized cable line information with a higher degree of suitability can be obtained. Also, because the total distance information corresponding to each initial cable line information is determined, each total distance information is added to each reference line optimization extraction data set corresponding to each initial cable line information, and each updated reference line optimization extraction data set containing distance information is obtained. Afterwards, each updated reference line optimization extraction data set and each initial cable line information are input into the artificial intelligence reasoning model to obtain optimized cable line information. This not only takes into account the impact of geographic spatial feature information on the cable line, but also takes into account the impact of total distance information on the cable line, thereby improving the feasibility of the cable line corresponding to the optimized cable line information obtained after optimization, reducing the number of times the initial cable line information is re-optimized, and reducing the waste of computer computing resources.

[0071] Step 107: Generate cable line laying information corresponding to the optimized cable line information based on the various geographic space feature information.

[0072] In some embodiments, the execution entity may generate cable line laying information corresponding to the optimized cable line information based on the various geographic spatial feature information.

[0073] In some optional implementations of some embodiments, the execution entity may generate cable line laying information corresponding to the optimized cable line information based on the various geographic spatial feature information through the following steps:

[0074] The first step is to determine the geographic space feature information corresponding to the candidate cable passing point identifiers included in the optimized cable line information as reference geographic space feature information.

[0075] In the second step, for each of the reference geographic spatial feature information, the soil condition information and the polluted area raster data included in the reference geographic spatial feature information are determined as the cable line laying sub-information.

[0076] The third step is to determine each determined cable line laying sub-information as the cable line laying information corresponding to the above-mentioned optimized cable line information.

[0077] Step 108: Generate cable laying method information corresponding to the optimized cable line information based on the cable line laying information.

[0078] In some embodiments, the execution entity may generate cable laying method information corresponding to the optimized cable line information based on the cable line laying information.

[0079] In some optional implementations of some embodiments, the execution entity may generate cable laying method information corresponding to the optimized cable line information based on the cable line laying information through the following steps:

[0080] The first step is to input the above-mentioned cable line laying information into a pre-trained cable laying method prediction model to obtain the cable laying method information corresponding to the above-mentioned optimized cable line information. The above-mentioned cable laying method prediction model can be a neural network model (for example, a BP neural network (back propagation neural network), a convolutional neural network (CNN)) that takes the cable line laying information as input and the cable laying method information as output. The above-mentioned cable laying method information can be text information indicating the cable laying method. For example, the above-mentioned cable laying method information can be a method of laying and installing cables to form a cable line. For example, the above-mentioned cable laying method information can be "laying the cables on a preset cable tray."

[0081] Optionally, the cable laying mode prediction model can be trained by the following steps:

[0082] The first step is to obtain a sample set, wherein the samples in the sample set include sample cable line laying information and sample target cable laying method information corresponding to the sample cable line laying information.

[0083] In the second step, the following training steps are performed based on the sample set:

[0084] Sub-step one: inputting the sample cable line laying information of at least one sample in the sample set into a preset initial neural network to obtain sample predicted cable laying mode information corresponding to each sample in the at least one sample.

[0085] Sub-step 2: Compare the sample predicted cable laying mode information corresponding to each of the at least one sample with the corresponding sample target cable laying mode information. In practice, the execution entity may use a cross-entropy loss function to determine the difference between the sample predicted cable laying mode information corresponding to each of the at least one sample and the corresponding sample target cable laying mode information. The distance may be represented by cosine similarity.

[0086] Sub-step three: determining whether the preset initial neural network achieves a preset optimization goal based on the comparison result. The above optimization goal may be minimizing the loss function or maximizing the likelihood function.

[0087] Sub-step four: in response to determining that the preset initial neural network achieves the above-mentioned optimization goal, the preset initial neural network is used as a trained cable laying method prediction model.

[0088] Sub-step 5: In response to determining that the preset initial neural network does not achieve the above-mentioned optimization goal, adjusting the network parameters of the preset initial neural network, using unused samples to form a sample set, and using the adjusted preset initial neural network as the preset initial neural network to perform the above-mentioned training step again. As an example, the network parameters of the preset initial neural network can be adjusted using a back propagation algorithm (BP algorithm).

[0089] Step 109: Determine the optimized cable line information and the cable laying method information as the cable line information, and display the cable line information on a preset cable line optimization page.

[0090] In some embodiments, the execution entity may determine the optimized cable line information and the cable laying method information as cable line information, and display the cable line information on a preset cable line optimization page. The preset cable line optimization page may be a page for displaying cable line information.

[0091] The above-mentioned embodiments of the present disclosure have the following beneficial effects: Through the cable route optimization method based on artificial intelligence reasoning models in some embodiments of the present disclosure, the availability of cable route information and the user experience of cable construction are improved. Specifically, the reason for the poor availability of cable route information and the user experience of cable construction is that the identifiers of each waypoint along the shortest distance path between the cable terminal field starting point and the cable terminal field terminal end point are directly used as cable route information, without considering the impact of geospatial feature information on the cable route. The shortest path may pass through complex terrain or obstacles, increasing the difficulty and risk of construction, resulting in poor availability of cable route information. At the same time, the identifiers of each waypoint along the shortest distance path between the cable terminal field starting point and the cable terminal field terminal end point are directly used as cable route information, without generating a cable laying method for the cable route based on the geospatial feature information of each waypoint, resulting in a poor user experience for cable construction. Based on this, the cable route optimization method based on artificial intelligence reasoning models in some embodiments of the present disclosure first obtains each candidate cable waypoint identifier corresponding to a preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field terminal identifier. Thus, candidate cable waypoint identifiers for the cable terminal field starting point, the cable terminal field terminal end point, and each waypoint between the two points can be obtained. Subsequently, geospatial feature information corresponding to each candidate cable waypoint identifier is retrieved from a preset database. Each of these geospatial feature information includes terrain elevation information at the waypoint, underground pipeline information at the waypoint, soil condition information at the waypoint, a housing distribution map at the waypoint, and polluted area raster data. Thus, geospatial feature information for the cable terminal field starting point, the cable terminal field terminal end point, and each waypoint between the two points can be obtained. Then, route optimization data extraction is performed on each of these geospatial feature information to obtain route optimization extraction datasets corresponding to each candidate cable waypoint identifier. Data extraction is performed on each of these geospatial feature information to obtain route optimization extraction datasets that influence the cable line. Next, pre-constructed cable waypoint identifier directed graph data corresponding to the preset cable terminal field identifier group and each candidate cable waypoint identifier is retrieved from the preset database. Thus, cable waypoint identifier directed graph data representing the connection relationship between the preset cable terminal field identifier group and each of the candidate cable waypoint identifiers can be obtained. Subsequently, the cable waypoint identifier directed graph corresponding to the cable waypoint identifier directed graph data is traversed to obtain the initial cable route information corresponding to the preset cable terminal field identifier group. Thus, initial cable route information representing all route paths between the cable terminal field starting point and the cable terminal field ending point can be obtained.Subsequently, based on the aforementioned line optimization extraction dataset and the pre-trained artificial intelligence inference model, each of the aforementioned initial cable line information is optimized to obtain optimized cable line information corresponding to the aforementioned preset cable terminal field identification group. Thus, by optimizing the line optimization extraction dataset that affects the cable line, each of the initial cable line information can be optimized to obtain optimized cable line information with higher availability. Subsequently, based on the aforementioned various geographic spatial feature information, cable line laying information corresponding to the aforementioned optimized cable line information is generated. Thus, cable line laying information for generating cable laying method information can be obtained. Next, based on the aforementioned cable line laying information, cable laying method information corresponding to the aforementioned optimized cable line information is generated. Thus, cable laying method information suitable for the optimized cable line information can be generated. The aforementioned optimized cable line information and the aforementioned cable laying method information are determined as cable line information, and the aforementioned cable line information is displayed on the preset cable line optimization page. Thus, the cable line information, including the optimized cable line information and the cable laying method information, can be displayed on the preset cable line optimization page, improving the user experience of cable construction users.

[0092] Further references Figure 2 As an implementation of the methods shown in the figures, the present disclosure provides some embodiments of a cable line optimization device based on an artificial intelligence reasoning model. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0093] like Figure 2As shown, the cable line optimization device 200 based on the artificial intelligence reasoning model of some embodiments includes: a first acquisition unit 201, a second acquisition unit 202, an extraction processing unit 203, a third acquisition unit 204, a traversal unit 205, an optimization processing unit 206, a first generation unit 207, a second generation unit 208 and a display unit 209. Among them, the first acquisition unit 201 is configured to obtain each candidate cable waypoint identifier corresponding to the preset cable terminal field identifier group, wherein the above-mentioned preset cable terminal field identifier group includes the cable terminal field starting point identifier and the cable terminal field end point identifier; the second acquisition unit 202 is configured to obtain each geographic spatial feature information corresponding to the above-mentioned each candidate cable waypoint identifier from the preset database, wherein each geographic spatial feature information in the above-mentioned each geographic spatial feature information includes the terrain elevation information of the waypoint, the underground pipeline information of the waypoint, the soil condition information of the waypoint, the house distribution map of the waypoint, and the pollution area raster data; the extraction and processing unit 203 is configured to perform line optimization data extraction processing on the above-mentioned each geographic spatial feature information to obtain the line optimization extraction data set corresponding to the above-mentioned each candidate cable waypoint identifier; the third acquisition unit 204 is configured to obtain the pre-constructed cable waypoint identifier corresponding to the above-mentioned preset cable terminal field identifier group and the above-mentioned each candidate cable waypoint identifier from the above-mentioned preset database. directed graph data; the traversal unit 205 is configured to traverse the cable route point identification directed graph corresponding to the above-mentioned cable route point identification directed graph data, and obtain each initial cable line information corresponding to the above-mentioned preset cable terminal field identification group; the optimization processing unit 206 is configured to optimize the above-mentioned each initial cable line information based on the above-mentioned line optimization extraction data set and the pre-trained artificial intelligence reasoning model, and obtain the optimized cable line information corresponding to the above-mentioned preset cable terminal field identification group; the first generation unit 207 is configured to generate the cable line laying information corresponding to the above-mentioned optimized cable line information based on the above-mentioned each geographic space feature information; the second generation unit 208 is configured to generate the cable laying method information corresponding to the above-mentioned optimized cable line information based on the above-mentioned cable line laying information; the display unit 209 is configured to determine the above-mentioned optimized cable line information and the above-mentioned cable laying method information as cable line information, and display the above-mentioned cable line information on the preset cable line optimization page.

[0094] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be repeated here.

[0095] Reference below Figure 3 , which shows a structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0096] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0097] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0098] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are performed.

[0099] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0100] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0101] The computer-readable medium may be contained in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When one or more programs are executed by the electronic device, the electronic device: obtains each candidate cable waypoint identifier corresponding to the preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field end point identifier; obtains each geographic spatial feature information corresponding to the above-mentioned candidate cable waypoint identifier from the preset database, wherein each of the above-mentioned geographic spatial feature information includes terrain elevation information of the waypoint, underground pipeline information of the waypoint, soil condition information of the waypoint, house distribution map of the waypoint, and polluted area raster data; performs line optimization data extraction processing on the above-mentioned each geographic spatial feature information to obtain a line optimization extraction data set corresponding to the above-mentioned each candidate cable waypoint identifier; obtains the above-mentioned preset cable terminal field identifier group and the above-mentioned data from the preset database. The pre-constructed cable waypoint identification directed graph data of each candidate cable waypoint identification; traversing the cable waypoint identification directed graph corresponding to the above cable waypoint identification directed graph data to obtain each initial cable line information corresponding to the above preset cable terminal field identification group; based on the above line optimization extraction data set and the pre-trained artificial intelligence reasoning model, optimizing the above each initial cable line information to obtain the optimized cable line information corresponding to the above preset cable terminal field identification group; based on the above each geographic space feature information, generating the cable line laying information corresponding to the above optimized cable line information; based on the above cable line laying information, generating the cable laying method information corresponding to the above optimized cable line information; determining the above optimized cable line information and the above cable laying method information as cable line information, and displaying the above cable line information on the preset cable line optimization page.

[0102] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0104] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, they may be described as: a processor including a first acquisition unit, a second acquisition unit, an extraction processing unit, a third acquisition unit, a traversal unit, an optimization processing unit, a first generation unit, a second generation unit, and a display unit. The names of these units do not, in some cases, constitute a limitation on the units themselves. For example, the first generation unit may also be described as "a unit for generating cable line laying information corresponding to the above-mentioned optimized cable line information based on the above-mentioned various geographic spatial feature information."

[0105] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0106] The above descriptions are merely some preferred embodiments of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of technical features, but should also encompass other technical solutions formed by any combination of technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing a feature with a technical feature having similar functions as disclosed in the embodiments of the present disclosure (but not limited to) can be formed.

Claims

1. A cable line optimization method based on an artificial intelligence reasoning model, comprising: Obtaining each candidate cable passing point identifier corresponding to a preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field end point identifier; Acquire each piece of geographic spatial feature information corresponding to each candidate cable waypoint identifier from a preset database, wherein each piece of geographic spatial feature information includes terrain elevation information of the waypoint, underground pipeline information of the waypoint, soil condition information of the waypoint, house distribution map of the waypoint, and polluted area raster data; Performing line optimization data extraction processing on each of the geographic space feature information to obtain a line optimization extraction data set corresponding to each of the candidate cable passing point identifiers; Acquire pre-constructed cable waypoint identification directed graph data corresponding to the preset cable terminal field identification group and each candidate cable waypoint identification from the preset database; Traversing the cable route point identification directed graph corresponding to the cable route point identification directed graph data to obtain each initial cable line information corresponding to the preset cable terminal field identification group; Based on the line optimization extraction data set and the pre-trained artificial intelligence reasoning model, each of the initial cable line information is optimized to obtain the optimized cable line information corresponding to the preset cable terminal field identification group; Based on the various geographic space feature information, generating cable line laying information corresponding to the optimized cable line information; Based on the cable line laying information, generating cable laying mode information corresponding to the optimized cable line information; The optimized cable line information and the cable laying method information are determined as cable line information, and the cable line information is displayed on a preset cable line optimization page.

2. The method according to claim 1, wherein: Each of the candidate cable waypoint identifiers corresponds to each of the geographic spatial feature information in one-to-one correspondence, and the line optimization data extraction processing is performed on each of the geographic spatial feature information to obtain a line optimization extraction data set corresponding to each of the candidate cable waypoint identifiers, including: For each piece of geospatial feature information in the respective geospatial feature information, the following extraction steps are performed: Determining the terrain elevation information of the waypoints included in the geographic space feature information as the terrain elevation information of the target waypoints; Determining the underground pipeline information of the waypoint included in the geographic space feature information as the underground pipeline information of the target waypoint; Determining the house distribution map of the waypoints included in the geographic space feature information as the house distribution map to be extracted; Performing identification and extraction processing on the house distribution map to be extracted to obtain the number of house distribution at the passing points; Determine the target waypoint terrain elevation information, the target waypoint underground pipeline information and the number of houses distributed at the waypoint as route optimization extraction data corresponding to the geographic space feature information; The determined individual line optimization extraction data are determined as a line optimization extraction data set, wherein each line optimization extraction data in the line optimization extraction data set corresponds to each candidate cable waypoint identifier in the individual candidate cable waypoint identifiers.

3. The method according to claim 1, wherein: The traversing of the cable route point identification directed graph corresponding to the cable route point identification directed graph data to obtain each initial cable line information corresponding to the preset cable terminal field identification group includes: Adding the cable terminal field starting point identifier in the cable path point identifier directed graph to a first preset sequence; Determine each node identifier corresponding to each direct successor node of the node corresponding to the cable terminal field starting point identifier in the cable path point identifier directed graph as each target candidate cable path point identifier; Based on each target candidate cable waypoint identifier and the first preset sequence, the following determination steps are performed: For each target candidate cable waypoint identifier in each target candidate cable waypoint identifier, perform the following steps: In response to determining that the target candidate cable waypoint identifier is a cable terminal field end identifier, adding the target candidate cable waypoint identifier to a first preset sequence to obtain a target first preset sequence; In response to determining that the target candidate cable waypoint identifier is not a cable terminal field end identifier, the following steps are performed: Adding the target candidate cable waypoint identifier to the first preset sequence to update the first preset sequence; In response to determining that each candidate cable waypoint identifier corresponding to each direct successor node of the node corresponding to the target candidate cable waypoint identifier in the cable waypoint identifier directed graph is not empty, determining each candidate cable waypoint identifier corresponding to each direct successor node of the node corresponding to the target candidate cable waypoint identifier in the cable waypoint identifier directed graph as each target candidate cable waypoint identifier, so as to update each target candidate cable waypoint identifier; Executing the determining step again according to the updated identifiers of each target candidate cable waypoint and the updated first preset sequence; The determined first preset sequences of each target are determined as each initial cable line information corresponding to the preset cable terminal field identification group, wherein each initial cable line information includes a cable waypoint identification sequence, the cable waypoint identification sequence includes each cable waypoint identification, and the each cable waypoint identification includes each candidate cable waypoint identification, a cable terminal field starting point identification and a cable terminal field end point identification.

4. The method according to claim 1, wherein: The generating of the cable line laying information corresponding to the optimized cable line information based on the various geographic space feature information includes: Determine each piece of geographic space feature information corresponding to each candidate cable passing point identifier included in the optimized cable line information as each piece of reference geographic space feature information; For each of the reference geographic spatial feature information, determining the soil condition information and the polluted area raster data included in the reference geographic spatial feature information as the cable line laying sub-information; The determined individual cable line laying sub-information is determined as the cable line laying information corresponding to the optimized cable line information.

5. The method according to claim 1, wherein: The generating, based on the cable line laying information, the cable laying mode information corresponding to the optimized cable line information comprises: The cable line laying information is input into a pre-trained cable laying method prediction model to obtain cable laying method information corresponding to the optimized cable line information.

6. The method according to claim 5, wherein: The cable laying method prediction model is trained by the following steps: Acquire a sample set, wherein the samples in the sample set include sample cable line laying information and sample target cable laying mode information corresponding to the sample cable line laying information; Perform the following training steps based on the sample set: Inputting the sample cable line laying information of at least one sample in the sample set into a preset initial neural network to obtain sample predicted cable laying mode information corresponding to each sample in the at least one sample; comparing the sample predicted cable laying mode information corresponding to each sample of the at least one sample with the corresponding sample target cable laying mode information; Determining whether the preset initial neural network achieves the preset optimization goal according to the comparison result; In response to determining that the preset initial neural network achieves the optimization goal, using the preset initial neural network as a trained cable laying mode prediction model; In response to determining that the preset initial neural network does not achieve the optimization goal, the network parameters of the preset initial neural network are adjusted, and unused samples are used to form a sample set, and the adjusted preset initial neural network is used as the preset initial neural network to perform the training step again.

7. A cable line optimization device based on an artificial intelligence reasoning model, comprising: A first acquisition unit is configured to acquire each candidate cable passing point identifier corresponding to a preset cable terminal field identifier group, wherein the preset cable terminal field identifier group includes a cable terminal field starting point identifier and a cable terminal field end point identifier; A second acquisition unit is configured to acquire, from a preset database, each piece of geographic spatial feature information corresponding to each of the candidate cable waypoint identifiers, wherein each of the various geographic spatial feature information includes terrain elevation information of the waypoint, underground pipeline information of the waypoint, soil condition information of the waypoint, house distribution map of the waypoint, and polluted area raster data; An extraction processing unit is configured to perform line optimization data extraction processing on each of the geographic space feature information to obtain a line optimization extraction data set corresponding to each of the candidate cable passing point identifiers; A third acquisition unit is configured to acquire pre-constructed cable waypoint identification directed graph data corresponding to the preset cable terminal field identification group and the respective candidate cable waypoint identifications from the preset database; A traversal unit is configured to traverse the cable route point identification directed graph corresponding to the cable route point identification directed graph data to obtain each initial cable line information corresponding to the preset cable terminal field identification group; An optimization processing unit is configured to optimize the initial cable line information based on the line optimization extraction data set and the pre-trained artificial intelligence reasoning model to obtain optimized cable line information corresponding to the preset cable terminal field identification group; A first generating unit is configured to generate cable line laying information corresponding to the optimized cable line information based on the respective geographic space feature information; A second generating unit is configured to generate cable laying mode information corresponding to the optimized cable line information based on the cable line laying information; The display unit is configured to determine the optimized cable line information and the cable laying method information as cable line information, and display the cable line information on a preset cable line optimization page.

8. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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