A method, device, system and storage medium for determining a water supply price reference index based on a large language model

Abstract

The present application relates to a method, device, system, and storage medium for determining a water supply price reference index based on a large language model. The method comprises obtaining a first water supply price language data sample; grouping individuals, deleting the first water supply price language data sample corresponding to invalid individual groups, and obtaining a second water supply price language data sample; parsing each second water supply price language data sample to obtain a parse tree; grouping the second water supply price language data samples according to the parse tree; calculating and sorting the group weights; calculating the influencing parameters of the parse tree and using the influencing parameters to correct the group weights, and using the corrected group weights as the water supply price reference index. The method, device, system, and storage medium for determining a water supply price reference index based on a large language model disclosed in the present application organize and analyze massive amounts of collected data and obtain results by cleaning and normalizing the collected data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, system and storage medium for determining a water supply price reference index based on a large language model. Background Art

[0002] The fundamental principles for determining water supply prices are cost coverage, reasonable returns, water conservation, and fair burden sharing. Local governments set water supply prices based on water supply costs, while also considering multiple factors, including local water resources, economic development, user affordability, and public feedback, before ultimately determining water supply prices.

[0003] Objective influencing factors such as water resource conditions, economic development and user affordability can be obtained by organizing, calculating and analyzing data from official public channels. However, public feedback is generated by different individuals and is difficult to obtain. A large sample size is required to obtain more accurate results, and analysis is still needed after obtaining the samples to obtain the results.

[0004] Manual analysis has limitations, primarily due to its slow processing speed and high cost of processing massive amounts of data, as well as the inclusion of a certain amount of useless information in the collected data. This limits its use to a small scale, and its lack of representativeness and accuracy.

[0005] It is also necessary to explore a technical solution that can quickly process massive public feedback samples. Summary of the Invention

[0006] The present application provides a method, device, system and storage medium for determining a water supply price reference index based on a large language model, which can organize, analyze and obtain results of massive collected data by cleaning and normalizing the collected data.

[0007] The above-mentioned purpose of this application is achieved through the following technical solutions:

[0008] In a first aspect, the present application provides a method for determining a water supply price reference index based on a large language model, comprising:

[0009] Obtaining a first water supply price language data sample, where the number of the first water supply price language data samples is multiple, and each first water supply price language data sample comes from an independent individual;

[0010] Grouping the individuals to obtain a valid individual group and an invalid individual group, deleting the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample;

[0011] Parse each second water supply price language data sample to obtain a parse tree;

[0012] Grouping the second water supply price language data samples according to the parse tree, the groups including a first sample group, a second sample group and a third sample group;

[0013] Calculate the grouping weight and sort it, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples;

[0014] Calculate the influence parameters of the parse tree and use the influence parameters to correct the grouping weights;

[0015] The adjusted grouping weights are used as the reference index for water supply prices.

[0016] In a possible implementation of the first aspect, grouping individuals includes:

[0017] Determine the first water supply price language data sample corresponding to the individual, and record it as the analysis sample;

[0018] Parsing the analysis sample to obtain the subjects included in the analysis sample;

[0019] Confirm the association relationship grid between the subjects and evaluate the association relationship grid to obtain the association relationship grid value;

[0020] Use the association grid values ​​to group individuals.

[0021] In a possible implementation of the first aspect, evaluating the association relationship grid to obtain the association relationship grid value includes:

[0022] Determine the nodes of the association relationship grid and the ownership relationship between the nodes;

[0023] Use nodes to generate a topological relationship graph in two-dimensional or three-dimensional space. The distance between two directly connected nodes is fixed, and the directions of two directly connected nodes are assigned according to their attributes.

[0024] Calculate the surface area of ​​the topological relationship graph and obtain the surface area value;

[0025] The surface area value of the topological relationship graph is used as the value of the association relationship grid.

[0026] In a possible implementation of the first aspect, parsing the second water supply price language data sample to obtain a parse tree includes:

[0027] Split the second water supply price language data sample into sentences;

[0028] Divide the sentence to obtain the subject and relation words included in the sentence;

[0029] Build a knowledge graph using subject and relation words and output the knowledge graph as a parse tree.

[0030] In a possible implementation of the first aspect, constructing a knowledge graph using subjects and relational words includes:

[0031] The subjects are divided into levels according to the relational terms, and each level includes at least one subject;

[0032] Construct a connection relationship between two subjects based on relational terms, where the two subjects belong to two adjacent levels;

[0033] When a subject has a connection relationship with multiple subjects at the next level, the same subjects in the multiple subjects at the next level are merged;

[0034] Assign directions to subjects according to their types. Subjects of the same type are assigned the same direction, and one direction is assigned to subjects of only one type.

[0035] Use Direction to adjust the orientation of the connection between the main body and the first body and the main body.

[0036] In a possible implementation of the first aspect, the method further includes:

[0037] Determine the branches of the knowledge graph, which include main branches and secondary branches;

[0038] Calculate the center segment of each main branch;

[0039] Drive the main branches of the knowledge graph to rotate so that the minimum distance between any two main branches belonging to the same knowledge graph is maximized;

[0040] Among them, in all knowledge graphs, subjects of the same type are given the same direction. When a subject in a knowledge graph is adjusted, subjects of the same type in other knowledge graphs are adjusted synchronously.

[0041] In a possible implementation of the first aspect, calculating the influencing parameters of the parse tree includes:

[0042] Construct a spatial model based on the parse tree and calculate the volume of the spatial model;

[0043] Determine the number of main branches of the parse tree;

[0044] The volume of the spatial model and the number of main branches of the tree are weighted to obtain the influence parameters of the parse tree.

[0045] In a second aspect, the present application provides a device for determining a water supply price reference index based on a large language model, comprising:

[0046] a data acquisition unit, configured to acquire a first water supply price language data sample, wherein the number of the first water supply price language data sample is multiple, and each first water supply price language data sample comes from an independent individual;

[0047] A first grouping unit is configured to group individuals to obtain a valid individual group and an invalid individual group, and delete the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample;

[0048] A first data processing unit is configured to parse each second water supply price language data sample to obtain a parse tree;

[0049] A second grouping unit is used to group the second water supply price language data samples according to the parse tree, the groups including a first sample group, a second sample group and a third sample group;

[0050] A first calculation unit is used to calculate and sort the grouping weights, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples;

[0051] A second calculation unit is used to calculate the influence parameters of the parse tree and use the influence parameters to correct the grouping weights;

[0052] The first result output unit is used to use the corrected grouping weight as a water supply price reference index.

[0053] In a third aspect, the present application provides a water supply price reference index determination system based on a large language model, the system comprising:

[0054] one or more memories for storing instructions; and

[0055] One or more processors, configured to call and execute the instructions from the memory to perform the method as described in the first aspect and any possible implementation of the first aspect.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising:

[0057] The program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0058] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0059] In a sixth aspect, the present application provides a chip system comprising a processor for implementing the functions involved in the above aspects, such as generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0060] The chip system may be composed of chips, or may include chips and other discrete devices.

[0061] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, connected via wired or wireless means, or the processor and the memory can be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic flowchart of the steps of a method for determining a water supply price reference index provided in this application.

[0063] Figure 2 This is a schematic diagram of obtaining the surface area value of a topological relationship diagram provided by the present application.

[0064] Figure 3 This is a schematic diagram of a processing process of a first water supply price language data sample provided by this application.

[0065] Figure 4 This is a schematic diagram of the cross-sectional area of ​​a branch in the extension direction provided by the present application, and the cross-sectional area is smaller than the reference value.

[0066] Figure 5 This is a schematic diagram of the cross-sectional area of ​​a branch in the extension direction provided by the present application, and the cross-sectional area is larger than the reference value. DETAILED DESCRIPTION

[0067] The technical solution in this application is further described in detail below with reference to the accompanying drawings.

[0068] See also Figure 1 In some examples, the specific steps of the method for determining a water supply price reference index based on a large language model disclosed in this application are as follows:

[0069] S101, obtaining a first water supply price language data sample, where the number of the first water supply price language data samples is multiple, and each first water supply price language data sample comes from an independent individual;

[0070] S102, grouping individuals to obtain a valid individual group and an invalid individual group, deleting the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample;

[0071] S103, parsing each second water supply price language data sample to obtain a parse tree;

[0072] S104, grouping the second water supply price language data samples according to the parse tree, the groups including a first sample group, a second sample group, and a third sample group;

[0073] S105, calculating and sorting the grouping weights, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples;

[0074] S106, calculating the influence parameters of the parse tree and correcting the grouping weights using the influence parameters;

[0075] S107: Using the corrected grouping weight as a water supply price reference index.

[0076] The content in step S101 and step S102, wherein the first water supply price language data sample refers to the different water price-related data content collected, needs to be grouped first. There are two groups here, namely the valid individual group and the invalid individual group. Then, the first water supply price language data sample corresponding to the invalid individual group is deleted to obtain the second water supply price language data sample.

[0077] The data content can be public opinion, articles, analytical reports, etc. In addition, the time period for data collection needs to be limited to avoid adverse effects caused by cross-time periods, because prices and individual perceptions of prices vary in different time periods.

[0078] For the first water supply price language data sample corresponding to the invalid individual group, generally speaking, only results such as price increase, price reduction, and some invalid content are released. For invalid content, blocked words can be used for filtering.

[0079] In some examples, individuals are grouped in the following ways:

[0080] S201, determining a first water supply price language data sample corresponding to an individual, recorded as an analysis sample;

[0081] S202, parsing the analysis sample to obtain the subject included in the analysis sample;

[0082] S203, confirming the association relationship grid between the subjects and evaluating the association relationship grid to obtain the association relationship grid value;

[0083] S204: Grouping individuals using the association relationship grid values.

[0084] In step S201 to step S204, an association relationship grid is constructed based on the association relationships between the subjects in the first water supply price language data sample, such as Figure 2 As shown, the correlation grid values ​​are then calculated and finally the correlation grid values ​​are used to group the individuals.

[0085] This is a quantifiable grouping method, and the specific explanation of the relevant content is as follows:

[0086] Obtaining the subject included in the analysis sample refers to determining the content that the analysis sample wants to express. This involves natural semantic processing. A suitable processing method is to first divide the analysis sample into units, where the length of a unit is one or two words, and then try to understand the meaning of the unit, and then understand the meaning of the sentence, until the meaning of the analysis sample is understood.

[0087] The purpose of unit division is to determine the composition of the analysis sample. The analysis sample consists of attributives, subjects, adverbials, predicates, objects, and complements. By dividing the analysis sample into units, we can determine the attributives, subjects, adverbials, predicates, objects, and complements. The main body included in the analysis sample refers to the subject and object.

[0088] After determining the subjects included in the analysis sample and the relationships between the subjects, the relationship grid can be obtained. Then the relationship grid is evaluated to obtain the relationship grid value. The specific process is as follows:

[0089] S301, determining the nodes of the association relationship grid and the ownership relationship between the nodes;

[0090] S302: Generate a topological relationship graph in a two-dimensional space or a three-dimensional space using nodes, wherein the distance between two directly connected nodes is fixed, and the two directly connected nodes are assigned directions according to attributes;

[0091] S303, calculating the surface area of ​​the topological relationship graph to obtain a surface area value;

[0092] S304: Using the surface area value of the topological relationship graph as the association relationship grid value.

[0093] The node in step S301 refers to the subject mentioned in steps S201 to S204. The way to use nodes to generate a topological relationship diagram in two-dimensional space or three-dimensional space is to assign coordinates to the nodes, and then determine the connection relationship between the nodes based on the ownership relationship between the nodes.

[0094] For the same and similar subjects, merging is also required.

[0095] The connection relationship here refers to a direct connection relationship. The distance between two nodes in a direct connection relationship is fixed, for example, both are a unit length. The two nodes in a direct connection relationship are assigned directions according to attributes.

[0096] According to the specific method of attribute allocation direction, for example, the first node and the second node are used to distinguish here. The first node and multiple second nodes have a connection relationship. The meanings of the multiple second nodes are different. The meaning of the second node here is the attribute between the two nodes.

[0097] A direction is assigned to each second node. When the distance between the two nodes is fixed, the position of the second node can be determined. Because the position of the second node can be determined by direction and distance under the premise that the position of the first node is determined.

[0098] At this time, a topological relationship diagram will be obtained in two-dimensional space or three-dimensional space, with the surface area value of the topological relationship diagram ( Figure 2 The area enclosed by the dotted line in the middle is used as the association grid value. Based on this, the method of grouping individuals using the association grid value is to specify a value, and individuals with a value greater than this value are classified into the valid individual group, and individuals with a value less than this value are classified into the invalid individual group.

[0099] It should be understood that for the first water supply price language data sample, the effective content cannot be determined simply by the number of words, because this will cause the second water supply price language data sample to be inaccurate. For example, a first water supply price language data sample has a large number of words, but it is merely an expression of personal emotions or a statement of a specific content, which makes the first water supply price language data sample have obvious tendencies and limitations.

[0100] In this application, this first water supply price language data sample needs to be discarded, and what needs to be retained is the content stated from multiple aspects, because the water supply price is affected by multiple factors and needs to be explained from multiple aspects.

[0101] After obtaining the second water supply price language data sample, step S103 will be executed. In this step, each second water supply price language data sample will be parsed to obtain a parse tree. Then, the second water supply price language data samples will be grouped according to the parse tree. The groups include the first sample group, the second sample group and the third sample group. Figure 3 , which is the content in step S104.

[0102] One function of the parse tree is to group data. After grouping, each group is calculated to obtain the final water supply price reference index. The parse tree determines the grouping basis by including keywords (water price increase, price reduction, maintenance or related).

[0103] In step S105, the grouping weight is calculated and sorted. The grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples. The role of the grouping weight is to make the sorting more influenced by both quantity and quality.

[0104] Because if we only consider the ranking from the perspective of quantity, the result will generally be no price increase or even a price decrease, because the vast majority of individuals express themselves based on their own positions.

[0105] Obviously, the results obtained at this time are not accurate, so they need to be corrected.

[0106] In step S106 , the influencing parameters of the parse tree are calculated and the grouping weights are corrected using the influencing parameters. Finally, in step S107 , the corrected grouping weights are used as the water supply price reference index.

[0107] In some examples, the second water supply price language data sample is parsed to obtain a parse tree in the following manner:

[0108] Split the second water supply price language data sample into sentences;

[0109] Divide the sentence to obtain the subject and relation words included in the sentence;

[0110] Build a knowledge graph using subject and relation words and output the knowledge graph as a parse tree.

[0111] At this time, it is still necessary to obtain the subject and relation words, and then use the subject and relation words to build a knowledge graph. The content of obtaining the subject and relation words has been stated in the previous content and will not be repeated here.

[0112] The specific way to construct a knowledge graph using subjects and relation words is:

[0113] The subjects are divided into levels according to the relational terms, and each level includes at least one subject;

[0114] Construct a connection relationship between two subjects based on relational terms, where the two subjects belong to two adjacent levels;

[0115] When a subject has a connection relationship with multiple subjects at the next level, the same subjects in the multiple subjects at the next level are merged;

[0116] Assign directions to subjects according to their types. Subjects of the same type are assigned the same direction, and one direction is assigned to subjects of only one type.

[0117] Use Direction to adjust the orientation of the connection between the main body and the first body and the main body.

[0118] The purpose of using direction here is to transform the knowledge graph from two-dimensional space to three-dimensional space. The core reason is that two-dimensional space is limited in expressing relationships. For example, when assigning directions to multiple entities at the next level, special consideration must be given to the smaller angle caused by the number of entities. This will cause the knowledge graph obtained in two-dimensional space to become crowded in the direction of expansion.

[0119] Furthermore, the following content has been added:

[0120] Determine the branches of the knowledge graph, which include main branches and secondary branches;

[0121] Calculate the center segment of each main branch;

[0122] Drive the main branches of the knowledge graph to rotate so that the minimum distance between any two main branches belonging to the same knowledge graph is maximized;

[0123] Among them, in all knowledge graphs, subjects of the same type are given the same direction. When a subject in a knowledge graph is adjusted, subjects of the same type in other knowledge graphs are adjusted synchronously.

[0124] It should be noted that after obtaining the knowledge graph, it needs to be normalized so that it can be compared and calculated. At this time, it is necessary to first determine the branches of the knowledge graph. Branches include main branches and secondary branches. The specific method of determining is based on the cross-sectional area of ​​the branch in the extension direction.

[0125] For cross-sectional area, see Figure 4 and Figure 5 The dotted line in the figure sets a reference value. Branches with a value greater than the reference value are considered primary branches, and branches with a value less than the reference value are considered secondary branches.

[0126] Then the center line segment of each main branch is calculated, and then the main branch of the knowledge graph is driven to rotate so that the minimum distance between any two main branches belonging to the same knowledge graph is maximized to show the maximum difference between the knowledge graphs.

[0127] In the above method, it is also necessary to assign the same direction to subjects of the same type in all knowledge graphs, and the purpose is also to achieve normalization of the knowledge graph.

[0128] In addition, when adjusting a subject in one knowledge graph, the same type of subjects in other knowledge graphs are adjusted synchronously. That is, for a subject of the same type, its orientation is the same in any knowledge graph. The minimum distance between any two main branches is the largest.

[0129] In some examples, the impact parameters of the parse tree are calculated as follows:

[0130] Construct a spatial model based on the parse tree and calculate the volume of the spatial model;

[0131] Determine the number of main branches of the parse tree;

[0132] The volume of the spatial model and the number of main branches of the tree are weighted to obtain the influence parameters of the parse tree.

[0133] In this method, two parameters are introduced, namely the spatial model volume and the number of main branches. The larger the spatial model volume, the more content the knowledge graph includes, and the larger the number of main branches, the more types of content the knowledge graph includes.

[0134] The weighted calculation method is to calculate by fixed value, the formula is as follows:

[0135] The influencing parameter of the parse tree = spatial model volume * N1 + number of main branches * N2, the sum of N1 and N2 is 1, both of which are preset values.

[0136] After obtaining the influencing parameters of the parse tree, the influencing parameters are used to correct the grouping weights. The specific method is to replace the number of samples of the second water supply price language data with the accumulation of the influencing parameters of the parse tree.

[0137] For example, the number of samples of the second water supply price language data is three, and the sample number value at this time is 3. The influence parameters of the obtained parse tree are: 1, 3, and 7 respectively. At this time, the cumulative influence parameters of the parse tree of the second water supply price language data sample are 11.

[0138] There are multiple second water supply price language data samples, and each second water supply price language data sample corresponds to an influencing parameter of a parse tree.

[0139] The influencing parameter of the parse tree can also be interpreted as the weight of the second water supply price language data sample.

[0140] Through the above method, a ratio of K1:K2:K3 can be obtained, where K1 corresponds to price increase (the accumulation of influencing parameters of the parse tree of the second water supply price language data sample in the first sample group), K2 corresponds to price reduction (the accumulation of influencing parameters of the parse tree of the second water supply price language data sample in the second sample group), and K3 corresponds to unchanged (the accumulation of influencing parameters of the parse tree of the second water supply price language data sample in the third sample group).

[0141] Through K1:K2:K3, we can further determine the corresponding proportions of price increases, price reductions and unchanged prices, and then choose the appropriate price adjustment plan based on this.

[0142] For example, combined with the content recorded in the previous article, K1:K2:K3 reflects the evaluation of water supply prices over a period of time. When determining the water supply price in the next stage, K1:K2:K3 is used as a reference factor. For example, if the proportion of people who agree to increase the price is high, you can consider raising the water supply price appropriately. If the proportion of people who disagree with the price increase is high, you need to choose not to adjust the water supply price or to raise it slightly.

[0143] It should also be noted that the water price reference index provided in this application is only used as one factor in determining water supply prices. It is a reference and cannot directly determine water supply prices. Because when determining water supply prices, the more key influencing factors are provided, the more reasonable the price setting will be.

[0144] This application also provides a device for determining a water supply price reference index based on a large language model, comprising:

[0145] a data acquisition unit, configured to acquire a first water supply price language data sample, wherein the number of the first water supply price language data sample is multiple, and each first water supply price language data sample comes from an independent individual;

[0146] A first grouping unit is configured to group individuals to obtain a valid individual group and an invalid individual group, and delete the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample;

[0147] A first data processing unit is configured to parse each second water supply price language data sample to obtain a parse tree;

[0148] A second grouping unit is used to group the second water supply price language data samples according to the parse tree, the groups including a first sample group, a second sample group and a third sample group;

[0149] A first calculation unit is used to calculate and sort the grouping weights, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples;

[0150] A second calculation unit is used to calculate the influence parameters of the parse tree and use the influence parameters to correct the grouping weights;

[0151] The first result output unit is used to use the corrected grouping weight as a water supply price reference index.

[0152] Furthermore, it also includes:

[0153] A first determining unit is used to determine a first water supply price language data sample corresponding to an individual, recorded as an analysis sample;

[0154] a second data processing unit, configured to parse the analysis sample to obtain a subject included in the analysis sample;

[0155] The second determining unit is used to confirm the association relationship grid between the subjects and evaluate the association relationship grid to obtain the association relationship grid value;

[0156] The grouping unit is used to group individuals using the associated relationship grid values.

[0157] Furthermore, it also includes:

[0158] A third determining unit is used to determine the nodes of the association relationship grid and the ownership relationship between the nodes;

[0159] A generating unit is used to generate a topological relationship graph in a two-dimensional space or a three-dimensional space using nodes, wherein the distance between two directly connected nodes is fixed, and the directions of the two directly connected nodes are assigned according to attributes;

[0160] The third calculation unit is used to calculate the surface area of ​​the topological relationship graph to obtain a surface area value;

[0161] The second result output unit is used to use the surface area value of the topological relationship graph as the association relationship grid value.

[0162] Furthermore, it also includes:

[0163] A sentence splitting unit, used for splitting the second water supply price language data sample into sentences;

[0164] A sentence division unit is used to divide the sentence into subjects and relational words included in the sentence;

[0165] The third result output unit is used to construct a knowledge graph using the subject and relation words and output the knowledge graph as a parse tree.

[0166] Furthermore, it also includes:

[0167] A level division unit, configured to divide the subjects into levels according to the relational terms, wherein each level includes at least one subject;

[0168] A relationship construction unit is used to construct a connection relationship between two subjects based on a relationship word, where the two subjects belong to two adjacent levels respectively;

[0169] a merging processing unit, configured to, when a subject has a connection relationship with multiple subjects at a next level, merge the same subjects in the multiple subjects at the next level;

[0170] A direction assigning unit is used to assign a direction to a subject according to the subject type. Subjects of the same type are assigned the same direction, and one direction is assigned to only one type of subject.

[0171] The direction adjustment unit is used to adjust the orientation of the connection between the main body, the first main body and the main body.

[0172] Furthermore, it also includes:

[0173] A first processing unit is used to determine branches of the knowledge graph, including main branches and secondary branches;

[0174] A second processing unit is used to calculate the center line segment of each main branch;

[0175] An adjustment unit, used to drive the main branches of the knowledge graph to rotate so that the minimum distance between any two main branches belonging to the same knowledge graph is maximized;

[0176] Among them, in all knowledge graphs, subjects of the same type are given the same direction. When a subject in a knowledge graph is adjusted, subjects of the same type in other knowledge graphs are adjusted synchronously.

[0177] Furthermore, it also includes:

[0178] A volume calculation unit, used to construct a spatial model based on the parse tree and calculate the volume of the spatial model;

[0179] A quantity determination unit, used to determine the number of main branches of the parse tree;

[0180] The weighted calculation unit is used to perform weighted calculation on the volume of the spatial model and the number of main branches of the tree to obtain the influence parameters of the parse tree.

[0181] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above method, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0182] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call programs. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0183] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical solutions.

[0184] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0186] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0187] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0188] It should also be understood that in various embodiments of this application, the terms "first," "second," and so on are merely used to indicate that multiple objects are distinct. For example, the terms "first time window" and "second time window" are merely used to indicate different time windows. They should not have any impact on the time windows themselves. The terms "first," "second," and so on should not limit the embodiments of this application in any way.

[0189] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0190] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned computer-readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0191] The present application also provides a water supply price reference index determination system based on a large language model, the system comprising:

[0192] one or more memories for storing instructions; and

[0193] One or more processors are used to call and execute the instructions from the memory to perform the method as described above.

[0194] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.

[0195] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0196] The chip system may be composed of chips, or may include chips and other discrete devices.

[0197] The processor mentioned in any of the above may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing a program for controlling the above-mentioned feedback information transmission method.

[0198] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, respectively, and connected via wired or wireless means to support the chip system in implementing the various functions of the above embodiments. Alternatively, the processor and the memory can be coupled on the same device.

[0199] Optionally, the computer instructions are stored in a memory.

[0200] Optionally, the memory is a storage unit within the chip, such as a register, cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0201] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0202] The non-volatile memory may be ROM, programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0203] Volatile memory can be RAM, which is used as an external cache memory. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0204] The embodiments of this specific implementation method are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for determining a water supply price reference index based on a large language model, characterized in that: include: Obtaining a first water supply price language data sample, where the number of the first water supply price language data samples is multiple, and each first water supply price language data sample comes from an independent individual; Grouping the individuals to obtain a valid individual group and an invalid individual group, deleting the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample; Parse each second water supply price language data sample to obtain a parse tree; The second water supply price language data sample is grouped according to the parse tree, and the groups include a first sample group, a second sample group, and a third sample group. The first sample group is a price increase group, the second sample group is a price reduction group, and the third sample group is a maintenance group. Calculate and sort the grouping weights, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples; Calculate the influence parameters of the parse tree and use the influence parameters to correct the grouping weights; The adjusted group weights are used as the reference index for water supply prices; The parameters that influence the calculation of the parse tree include: Construct a spatial model based on the parse tree and calculate the volume of the spatial model; Determine the number of main branches of the parse tree; The volume of the spatial model and the number of main branches of the tree are weighted to obtain the influence parameters of the parse tree.

2. The method for determining a water supply price reference index based on a large language model according to claim 1, characterized in that: Grouping individuals involves: Determine the first water supply price language data sample corresponding to the individual, and record it as the analysis sample; Parsing the analysis sample to obtain the subjects included in the analysis sample; Confirm the association relationship grid between the subjects and evaluate the association relationship grid to obtain the association relationship grid value; Use the association grid values ​​to group individuals.

3. The method for determining a water supply price reference index based on a large language model according to claim 2, characterized in that: Evaluate the association relationship grid and obtain the association relationship grid values ​​including: Determine the nodes of the association relationship grid and the ownership relationship between the nodes; Use nodes to generate a topological relationship graph in two-dimensional or three-dimensional space. The distance between two directly connected nodes is fixed, and the directions of two directly connected nodes are assigned according to their attributes. Calculate the surface area of ​​the topological relationship graph and obtain the surface area value; The surface area value of the topological relationship graph is used as the value of the association relationship grid.

4. The method for determining a water supply price reference index based on a large language model according to any one of claims 1 to 3, characterized in that: The second water supply price language data sample is parsed to obtain a parse tree including: Split the second water supply price language data sample into sentences; Divide the sentence to obtain the subject and relation words included in the sentence; Build a knowledge graph using subject and relation words and output the knowledge graph as a parse tree.

5. The method for determining a water supply price reference index based on a large language model according to claim 4, characterized in that: Building a knowledge graph using subject and relation words includes: The subjects are divided into levels according to the relational terms, and each level includes at least one subject; Construct a connection relationship between two subjects based on relational terms, where the two subjects belong to two adjacent levels; When a subject has a connection relationship with multiple subjects at the next level, the same subjects in the multiple subjects at the next level are merged; Assign directions to subjects according to their types. Subjects of the same type are assigned the same direction, and one direction is assigned to subjects of only one type. Use Direction to adjust the orientation of the connection between the main body and the first body and the main body.

6. The method for determining a water supply price reference index based on a large language model according to claim 5, characterized in that: Also includes: Determine the branches of the knowledge graph, which include main branches and secondary branches; Calculate the center segment of each main branch; Drive the main branches of the knowledge graph to rotate so that the minimum distance between any two main branches belonging to the same knowledge graph is maximized; Among them, in all knowledge graphs, subjects of the same type are given the same direction. When a subject in a knowledge graph is adjusted, subjects of the same type in other knowledge graphs are adjusted synchronously.

7. A device for determining a water supply price reference index based on a large language model, characterized in that: include: a data acquisition unit, configured to acquire a first water supply price language data sample, wherein the number of the first water supply price language data sample is multiple, and each first water supply price language data sample comes from an independent individual; A first grouping unit is configured to group individuals to obtain a valid individual group and an invalid individual group, and delete the first water supply price language data sample corresponding to the invalid individual group to obtain a second water supply price language data sample; A first data processing unit is configured to parse each second water supply price language data sample to obtain a parse tree; A second grouping unit is used to group the second water supply price language data sample according to the parse tree, the grouping including a first sample group, a second sample group and a third sample group, the first sample group is a price increase group, the second sample group is a price reduction group, and the third sample group is a maintenance group; A first calculation unit is used to calculate and sort the grouping weights, where the grouping weight is the ratio of the number of second water supply price language data samples in the group to the total number of second water supply price language data samples; A second calculation unit is used to calculate the influence parameters of the parse tree and use the influence parameters to correct the grouping weights; A first result output unit, configured to use the corrected grouping weight as a water supply price reference index; The parameters that influence the calculation of the parse tree include: Construct a spatial model based on the parse tree and calculate the volume of the spatial model; Determine the number of main branches of the parse tree; The volume of the spatial model and the number of main branches of the tree are weighted to obtain the influence parameters of the parse tree.

8. A water supply price reference index determination system based on a large language model, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium comprises: The program, when executed by a processor, executes the method according to any one of claims 1 to 6.

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