Supply and demand identification method and device for public service facility, equipment, medium and product

Abstract

The invention discloses a supply and demand identification method and device for public service facilities, equipment, a medium and a product. The method comprises the following steps: acquiring related data of a target area; preprocessing the related data to obtain supply and demand prediction data of the target public service facility; according to the supply and demand prediction data and a trained cumulative preference evaluation model, predicting a preference value of each user in the target area for the target public service facility; and according to the preference value, determining a supply demand of the target public service facility so as to plan the target public service facility. According to the method, the preference value of the target area user for the target public service facility can be predicted based on the current public service facility data, the road network and traffic data and the population data, the supply demand of the target public service facility is determined, and the public service facility is finely planned according to the people-oriented demand, so that the public service facility planning is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of public service facilities, and in particular, to a method, device, equipment, medium and product for identifying the supply and demand of public service facilities. Background Art

[0002] Public service facilities are facilities that provide various public service products for urban residents. It is a spatial carrier for carrying public services and an important part of ensuring the normal operation of society and the quality of residents' lives. The research and planning of public service facilities are important ways to achieve the optimal allocation of resources. Its research has very important practical significance for promoting urban-rural integration and integrated development. Good public service facilities can not only improve the happiness and satisfaction of residents, but also attract more talents and investments, injecting new vitality into the economic development of the city. At present, there are large differences in the needs of public service facility planning among different regions and different groups. At the same time, with the development of the urbanization process, the urban population is always in dynamic evolution, requiring urban planning to pay more attention to the individualized needs of individuals. The past static planning concept and the public service facility planning method that determines supply based on total quantity are difficult to adapt to the new situation of current urban planning and design and do not meet the needs of refined planning. Therefore, it is necessary to optimize the planning of public service facilities. Summary of the Invention

[0003] The present invention provides a method, device, equipment, medium and product for identifying the supply and demand of public service facilities. Based on the current public service facility data, road network and traffic data, and population data, it predicts the preference value of users in the target area for the target public service facility, determines the supply and demand of the target public service facility, and thus optimizes the planning of public service facilities.

[0004] To achieve the above object, an embodiment of the present invention provides a method for identifying the supply and demand of public service facilities, including:

[0005] Obtain relevant data of the target area; wherein, the relevant data includes the public service facility data, road network and traffic data, and population data of the target area;

[0006] Preprocess the relevant data to obtain the supply and demand prediction data of the target public service facility;

[0007] According to the supply and demand prediction data and the trained cumulative preference evaluation model, predict the preference value of each user in the target area for the target public service facility;

[0008] According to the preference value, determine the supply and demand of the target public service facility to plan the target public service facility.

[0009] As an improvement to the above solution, the preprocessing of the relevant data to obtain the supply and demand prediction data of the target public service facilities includes:

[0010] Match the coordinates and elevations of the relevant data and unify the data format to obtain the standardized relevant data;

[0011] Convert the standardized public service facility data and population data to obtain the target public service facility information and user personal information of the target area;

[0012] According to the standardized road network and traffic data, traverse the standardized public service facility data and population data, calculate the traffic connections between the standardized public service facility data and population data, and obtain the corresponding traffic connection information;

[0013] According to the target public service facility information, user personal information, and the corresponding traffic connection information, obtain the supply and demand prediction data of the target public service facilities.

[0014] As an improvement to the above solution, the determining the supply and demand of the target public service facilities according to the preference value to plan the target public service facilities includes:

[0015] Calculate the service demand share of each user for the target public service facilities according to the preference value;

[0016] Determine the supply and demand of the target public service facilities according to the service demand share;

[0017] Plan the target public service facilities according to the supply and demand of the target public service facilities and the current distribution situation.

[0018] As an improvement to the above solution, before according to the supply and demand prediction data and the trained cumulative preference evaluation model, the method further includes:

[0019] Establish and train a cumulative preference evaluation model to obtain a trained cumulative preference evaluation model.

[0020] As an improvement to the above solution, the establishing and training a cumulative preference evaluation model to obtain a trained cumulative preference evaluation model includes:

[0021] Obtain the relevant sample data of a specific area in the target area; wherein, the relevant sample data includes the public service facility data, road network and traffic data, population data, and population preference data of the specific area;

[0022] Preprocess the relevant sample data to obtain the sample predicted supply and demand data of the public service facilities in the specific area;

[0023] Build a cumulative preference evaluation model, and use the sample predicted supply and demand data as the training sample set of the cumulative preference evaluation model;

[0024] According to the training sample set, use the KNN algorithm to train the cumulative preference evaluation model to obtain a trained cumulative preference evaluation model.

[0025] As an improvement of the above solution, the step of using the KNN algorithm to train the cumulative preference evaluation model according to the training sample set to obtain a trained cumulative preference evaluation model includes:

[0026] Use the public service facility information, user personal information, and corresponding traffic connection information in the training sample set as independent variables, and use the population preference value in the training sample set as the dependent variable;

[0027] Use the KNN algorithm to calculate the relationship between the independent variable and the dependent variable, and perform classification training on the cumulative preference evaluation model to obtain a trained cumulative preference evaluation model.

[0028] To achieve the above object, an embodiment of the present invention provides a supply and demand identification device for public service facilities, including:

[0029] A relevant data acquisition module, configured to acquire relevant data of a target area; wherein, the relevant data includes public service facility data, road network and traffic data, and population data of the target area;

[0030] A predicted data acquisition module, configured to preprocess the relevant data to obtain supply and demand prediction data of a target public service facility;

[0031] A preference data prediction module, configured to predict the preference value of each user in the target area for the target public service facility according to the supply and demand prediction data and the trained cumulative preference evaluation model;

[0032] A service facility planning module, configured to determine the supply and demand of the target public service facility according to the preference value, so as to plan the target public service facility.

[0033] To achieve the above object, an embodiment of the present invention correspondingly provides a supply and demand identification device for public service facilities, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned supply and demand identification method for public service facilities is implemented.

[0034] To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above-mentioned supply and demand identification method for public service facilities.

[0035] To achieve the above object, an embodiment of the present invention further provides a computer program product, where the computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the above-mentioned supply and demand identification method for public service facilities.

[0036] Compared with the prior art, a supply and demand identification method, device, equipment, medium and product for public service facilities disclosed in an embodiment of the present invention obtain relevant data of a target area; where the relevant data includes public service facility data, road network and traffic data, and population data of the target area; preprocess the relevant data to obtain supply and demand prediction data of a target public service facility; predict the preference value of each user in the target area for the target public service facility according to the supply and demand prediction data and a trained cumulative preference evaluation model; determine the supply and demand of the target public service facility according to the preference value, so as to plan the target public service facility. It can predict the preference value of users in the target area for the target public service facility based on the current public service facility data, road network and traffic data, and population data, determine the supply and demand of the target public service facility, and refine the planning of public service facilities, thereby optimizing the planning of public service facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic flowchart of a supply and demand identification method for public service facilities provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic structural diagram of a supply and demand identification device for public service facilities provided by an embodiment of the present invention;

[0039] Figure 3 is a schematic block diagram of a supply and demand identification device for public service facilities provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be noted that the terms "including" and "specific" in the present invention, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0042] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for identifying the supply and demand of public service facilities provided by an embodiment of the present invention. The method for identifying the supply and demand of public service facilities includes:

[0043] S1. Obtain relevant data of the target area; wherein, the relevant data includes public service facility data, road network and traffic data, and population data of the target area;

[0044] S2. Preprocess the relevant data to obtain supply and demand prediction data of the target public service facility;

[0045] S3. According to the supply and demand prediction data and the trained cumulative preference evaluation model, predict the preference value of each user in the target area for the target public service facility;

[0046] S4. According to the preference value, determine the supply demand of the target public service facility to plan the target public service facility.

[0047] Exemplarily, the method for identifying the supply and demand of public service facilities described in the embodiment of the present invention can be implemented by a facility management server. The facility management server can interact with target users and with a city planning platform. The facility management server obtains relevant data of the target area (such as public service facility data, road network and traffic data, and population data); performs coordinate and elevation matching and normalization processing on the relevant data to obtain supply and demand prediction data of the target public service facility; predicts the preference value of each user in the target area for the target public service facility according to the supply and demand prediction data and the trained cumulative preference evaluation model; to determine the supply demand of each user for the target public service facility, and plan the target public service facility according to the supply demand and the current distribution of the target public service facility, which can finely plan the public service facility, thereby optimizing the public service facility planning.

[0048] Specifically, the step S2 includes:

[0049] S21. Perform coordinate and elevation matching on the relevant data and unify the data format to obtain standardized relevant data;

[0050] S22. Convert the standardized public service facility data and population data to obtain the target public service facility information and user personal information of the target area;

[0051] S23. Based on the standardized road network and traffic data, traverse the standardized public service facility data and population data, calculate the traffic connection between the standardized public service facility data and population data, and obtain the corresponding traffic connection information;

[0052] S24. Based on the target public service facility information, user personal information, and the corresponding traffic connection information, obtain the supply and demand prediction data of the target public service facility.

[0053] Exemplarily, collect multi-source big data (relevant data) of the target area in an open-source data platform, including urban public service facility data, urban road network and traffic data, urban population data, etc.; import the collected multi-source big data into a geographic information platform, and use a spatial correction tool to match the coordinates and elevations of the multi-source spatial big data and unify the data format; standardize the processed data. Specifically: regarding the target public service facility as the supply subject, convert its data into point elements containing information such as facility level, facility category, facility age, and facility scale, and after normalizing the attributes or values of each piece of information, record it as the target public service facility information; regarding the urban population as the demand individual, convert its data into point elements containing information such as income, gender, age, occupation, and education level, and after normalizing the attributes or values of each piece of information, record it as the user personal information; at the same time, traverse all facility and population points, calculate the connection relationship between the two, record traffic connection information such as traffic time consumption, traffic cost, and traffic comfort level, and after normalizing, record it as the traffic connection information, so as to obtain the supply and demand prediction data of the target public service facility.

[0054] Specifically, step S4 includes:

[0055] S41. Calculate the service demand share of each user for the target public service facility according to the preference value;

[0056] S42. Determine the supply demand of the target public service facility according to the service demand share;

[0057] S43. Plan the target public service facility according to the supply demand and current distribution of the target public service facility.

[0058] Exemplarily, if only predicting for sports public service facilities, samples related to educational facilities, medical facilities, etc. in the sample set can be masked; according to the preference value P of each user in the target area for the target public service facility (sports public service facility) ij ; with P ijAssign the service capacity of the target public service facility i to the related user j as a weight factor, and the calculation formula is:

[0059]

[0060] Among them, S i represents the service capacity of the target public service facility i, expressed in the usable area of the facility (unit: square meters); P ij represents the overall preference (preference value) of user j for the target public service facility i; S ij represents the service capacity of the target public service facility i assigned to user j (for example, the usable area of the facility).

[0061] Calculate the total service capacity obtained by user j (for example, the total facility service area), and the calculation formula is:

[0062] S j =∑ i S ij ,

[0063] Among them, S j represents the total service capacity obtained by user j.

[0064] Summarize the service area of the specific facilities obtained by the specific group according to the actual planning and design requirements, compare the summarized value with the planned target value, study and determine the supply and demand balance of the public service facilities for the specific facilities and groups, and judge the friendliness of the target public service facilities in the target area to an individual or a certain group; integrate the current situation information such as the planned scope of the target area, the current distribution of the target public service facilities, and the current urban population distribution, and the friendliness of the target public service facilities to an individual or a certain group to generate a report on the service situation of the target public service facilities; generate an optimization strategy for the public service facilities according to the report on the service situation of the target public service facilities, and plan the target public service facilities. For example, summarize the service area of the street-level cultural service facilities obtained by the low-income female labor group, or summarize the service area of the community-level sports service facilities obtained by the retired elderly group, etc., to analyze the service friendliness of this type of specific public service facilities to this type of specific group, and formulate targeted optimization strategies based on this.

[0065] Furthermore, before the supply and demand prediction data and the trained cumulative preference evaluation model are used, the method further includes:

[0066] S01. Establish and train a cumulative preference evaluation model to obtain a trained cumulative preference evaluation model.

[0067] Specifically, the step S01 includes:

[0068] S011. Obtain the relevant sample data of a specific area in the target area; wherein, the relevant sample data includes public service facility data, road network and traffic data, population data, and population preference data of the specific area;

[0069] S012. Preprocess the relevant sample data to obtain the sample predicted supply and demand data of public service facilities in the specific area;

[0070] S013. Establish a cumulative preference evaluation model, and use the sample predicted supply and demand data as the training sample set of the cumulative preference evaluation model;

[0071] S014. According to the training sample set, use the KNN algorithm to train the cumulative preference evaluation model to obtain the trained cumulative preference evaluation model.

[0072] Specifically, the step of using the KNN algorithm to train the cumulative preference evaluation model according to the training sample set to obtain the trained cumulative preference evaluation model includes:

[0073] Take the public service facility information, user personal information, and corresponding traffic connection information in the training sample set as independent variables, and take the population preference value in the training sample set as the dependent variable;

[0074] Use the KNN algorithm to calculate the relationship between the independent variables and the dependent variable, and perform classification training on the cumulative preference evaluation model to obtain the trained cumulative preference evaluation model.

[0075] Exemplarily, collect multi-source big data of a specific area in an open-source data platform and collect population preference data through a questionnaire survey. The multi-source big data includes urban public service facility data, urban road network and traffic data, urban population data, etc.; the information collected by the questionnaire includes the personal information O of the sample users i , and the preference information of the interviewed sample users for certain public service facilities, and normalize the preference information to a value between 0 and 10 and record it as P. The higher the value of P, the higher the preference value of the interviewee for the facility; import the collected multi-source big data into the geographic information platform, and use the spatial correction tool to match the coordinates and elevations of the multi-source spatial big data and unify the data format; standardize the processed data. Specifically: take public service facilities as the supply main body, and convert its data into point elements containing information such as facility level, facility category, facility newness and facility scale. After normalizing the attributes or values of each information, record it as sample public service facility information S i ; take urban population as the demand individual, and convert its data into point elements containing information such as income, gender, age, occupation, education level, etc. After normalizing the attributes or values of each information, record it as sample user personal information O i; Traverse all facilities and population points simultaneously, calculate the connection relationship between the two, record connection information such as traffic time consumption, traffic cost, and traffic comfort level, and after normalization, record it as sample traffic connection information C i ; Take the sample user personal information O i and the corresponding sample public service facility information S i and the sample traffic connection information C between the two i together as independent variables, denoted as X, X = O i + S i + C i , the dimension of X is the total quantity of O i , S i , C i . Denote it as an n-dimensional vector. Take the normalized preference information P as the dependent variable to construct a training sample set; adopt the KNN algorithm, establish the feature space of the input vector X, calculate the relationship between the input vector X and the predicted value P, conduct classification model training, and divide the training samples into k categories. For any n-dimensional input vector m, this model finds the point closest to it in the feature space of the vector X, and the output is the predicted value Pm (preference value) corresponding to the feature vector m. Adjust the parameters of the cumulative preference evaluation model to obtain the trained cumulative preference evaluation model.

[0076] The embodiment of the present invention saves a large amount of manpower and material resources, and at the same time greatly reduces the interference of external factors in the planning process, realizing the high efficiency, transparency and stability of the whole planning process; and all data are obtained from open source platforms to obtain data of the urban planning area in reality, reflecting the real layout of sports facilities in real life. The calculation logic of the algorithm model is clear, and it can be appropriately adjusted according to the different population preferences in different cities and regions and different types of public service facilities to adapt to the development needs of different cities and different public service facilities, realizing wide applicability.

[0077] A supply-demand identification method for public service facilities disclosed in an embodiment of the present invention includes obtaining relevant data of a target area; wherein, the relevant data includes public service facility data, road network and traffic data, and population data of the target area; preprocessing the relevant data to obtain supply-demand prediction data of a target public service facility; predicting a preference value of each user in the target area for the target public service facility according to the supply-demand prediction data and a trained cumulative preference evaluation model; and determining the supply-demand of the target public service facility according to the preference value so as to plan the target public service facility. It can predict the preference value of users in the target area for the target public service facility based on the existing public service facility data, road network and traffic data, and population data, determine the supply-demand of the target public service facility, and refine the planning of public service facilities, thereby optimizing the planning of public service facilities. By sorting out the preferences of public service facilities per capita and designing algorithms, it pays attention to the differentiated demands of different groups for various public service facilities. It helps to more accurately grasp the service situation of public service facilities, provide more targeted suggestions for subsequent urban planning and facility optimization, and achieve refined management and control of the city.

[0078] See Figure 2 , Figure 2 FIG. is a schematic structural diagram of a supply-demand identification device 10 for public service facilities provided by an embodiment of the present invention. The supply-demand identification device 10 for public service facilities includes:

[0079] A relevant data acquisition module 11, configured to obtain relevant data of a target area; wherein, the relevant data includes public service facility data, road network and traffic data, and population data of the target area;

[0080] A prediction data acquisition module 12, configured to preprocess the relevant data to obtain supply-demand prediction data of a target public service facility;

[0081] A preference data prediction module 13, configured to predict a preference value of each user in the target area for the target public service facility according to the supply-demand prediction data and a trained cumulative preference evaluation model;

[0082] A service facility planning module 14, configured to determine the supply-demand of the target public service facility according to the preference value so as to plan the target public service facility.

[0083] Further, the supply-demand identification device 10 for public service facilities further includes:

[0084] A preference model construction module, configured to establish and train a cumulative preference evaluation model to obtain a trained cumulative preference evaluation model.

[0085] The supply and demand identification device 10 of a public service facility provided by an embodiment of the present invention can implement all the processes of the supply and demand identification method of the public service facility in the above embodiment. The functions of each module in the device and the achieved technical effects are respectively the same as the functions and the achieved technical effects of the supply and demand identification method of the public service facility in the above embodiment, and will not be elaborated here.

[0086] See Figure 3 , Figure 3 which is a schematic structural diagram of a supply and demand identification device 20 of a public service facility provided by an embodiment of the present invention. The supply and demand identification device 20 of the public service facility in this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above embodiment of the supply and demand identification method of the public service facility are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the above embodiment of the supply and demand identification device of the public service facility are implemented.

[0087] Exemplarily, the computer program can be divided into one or more modules. The one or more modules are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the supply and demand identification device 20 of the public service facility.

[0088] The supply and demand identification device 20 of the public service facility can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The supply and demand identification device 20 of the public service facility can include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the supply and demand identification device 20 of the public service facility, and does not constitute a limitation on the supply and demand identification device 20 of the public service facility. It may include more or fewer components than shown, or combine certain components, or different components. For example, the supply and demand identification device 20 of the public service facility may further include an input / output device, a network access device, a bus, etc.

[0089] The so-called processor 21 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the supply and demand identification device 20 of the public service facility, and connects various parts of the supply and demand identification device 20 of the entire public service facility through various interfaces and lines.

[0090] The memory 22 can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory 22, and by calling the data stored in the memory 22, the processor 21 realizes various functions of the supply and demand identification device 20 of the public service facility. The memory 22 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0091] Among them, if the modules integrated in the supply and demand identification device 20 of the public service facilities 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 such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0092] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0093] The embodiment of the present invention also provides a computer-readable storage medium, and the computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the supply and demand identification method of the public service facilities as described in the above embodiment.

[0094] In addition, the embodiment of the present invention also provides a computer program product. The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the supply and demand identification method of the public service facilities as described in the above embodiment.

[0095] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for identifying supply and demand of public service facilities, characterized in that: include: Acquire relevant data of the target area; wherein the relevant data includes public service facility data, road network and traffic data, and population data of the target area; Preprocessing the relevant data to obtain supply and demand forecast data of target public service facilities; Predicting the preference value of each user in the target area for the target public service facility based on the supply and demand forecast data and the trained cumulative preference evaluation model; According to the preference value, the supply demand of the target public service facility is determined to plan the target public service facility.

2. The method for identifying supply and demand of public service facilities according to claim 1, characterized in that: The preprocessing of the relevant data to obtain supply and demand forecast data of the target public service facility includes: Matching the coordinates and elevations of the relevant data, and unifying the data format to obtain standardized relevant data; Performing data conversion on the standardized public service facility data and population data to obtain target public service facility information and user personal information of the target area; According to the standardized road network and traffic data, the standardized public service facility data and population data are traversed, the traffic connection between the standardized public service facility data and population data is calculated, and the corresponding traffic connection information is obtained; According to the target public service facility information and user personal information, as well as corresponding transportation contact information, supply and demand forecast data of the target public service facility is obtained.

3. The method for identifying supply and demand of public service facilities according to claim 1, characterized in that: Determining the supply demand of the target public service facility according to the preference value to plan the target public service facility includes: Calculating the service demand share of each user for the target public service facility according to the preference value; Determining the supply demand of the target public service facility according to the service demand share; The target public service facilities are planned according to the supply demand and current distribution of the target public service facilities.

4. The method for identifying supply and demand of public service facilities according to claim 1, characterized in that: Before evaluating the model based on the supply and demand forecast data and the trained cumulative preference, the method further includes: A cumulative preference evaluation model is established and trained to obtain a trained cumulative preference evaluation model.

5. The method for identifying supply and demand of public service facilities according to claim 4, characterized in that: The step of establishing and training the cumulative preference evaluation model to obtain the trained cumulative preference evaluation model includes: Acquire relevant sample data of a specific area of ​​the target region; wherein the relevant sample data includes public service facility data, road network and traffic data, population data, and population preference data of the specific area; Preprocessing the relevant sample data to obtain sample forecast supply and demand data of public service facilities in the specific area; Establishing a cumulative preference evaluation model, and using the sample forecast supply and demand data as a training sample set for the cumulative preference evaluation model; According to the training sample set, the cumulative preference evaluation model is trained using the KNN algorithm to obtain a trained cumulative preference evaluation model.

6. The method for identifying supply and demand of public service facilities according to claim 5, characterized in that: The method of training the cumulative preference evaluation model using the KNN algorithm according to the training sample set to obtain the trained cumulative preference evaluation model includes: Using the public service facility information, user personal information, and corresponding transportation contact information in the training sample set as independent variables, and using the population preference value in the training sample set as a dependent variable; The KNN algorithm is used to calculate the relationship between the independent variable and the dependent variable, and the cumulative preference evaluation model is classified and trained to obtain a trained cumulative preference evaluation model.

7. A supply and demand identification device for public service facilities, characterized in that: include: A relevant data acquisition module is used to acquire relevant data of the target area; wherein the relevant data includes public service facility data, road network and traffic data, and population data of the target area; A forecast data acquisition module, used to pre-process the relevant data to obtain supply and demand forecast data of the target public service facility; A preference data prediction module, used to predict the preference value of each user in the target area for the target public service facility based on the supply and demand prediction data and the trained cumulative preference evaluation model; The service facility planning module is used to determine the supply demand of the target public service facility according to the preference value so as to plan the target public service facility.

8. A supply and demand identification device for public service facilities, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for identifying supply and demand of a public service facility as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the supply and demand identification method for public service facilities as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the supply and demand identification method of a public service facility as described in any one of claims 1 to 6.

Images