Project multi-organization collaborative management method, system and device

Abstract

The embodiment of the invention provides a project multi-organization collaborative management method, system and device. The method comprises the following steps: acquiring unit characteristic factors of a plurality of participants of a construction project; the unit characteristic factors comprise the organization scale, the organization property, the organization type, the organization area and the organization level of each participant; determining a collaborative structure scale of the construction project based on the unit characteristic factors; determining the project type of the construction project based on the unit characteristic factor and the collaborative structure scale; determining a collaborative structure mode corresponding to the construction project based on the historical project data; and generating a collaborative management structure of the construction project based on the collaborative structure scale, the project type and the collaborative structure mode.

Description

Technical Field

[0001] This specification relates to the field of project management, and in particular to a method, system and device for multi-organization collaborative management of projects. Background Art

[0002] Large-scale projects usually involve multiple units. For example, in the construction industry, there are construction units, construction units, supervision units, design units, and suppliers. Traditional project management systems usually divide the organizational structure by department, which cannot directly reflect the relationship between personnel and units. As a result, the organizational structure of each participating unit in the project is independent and the data is fragmented, making it difficult to achieve effective collaborative management. In addition, the organizational structure established between units and projects is often independent, forming a data island, which cannot meet the needs of modern complex project management for organizational and personnel information penetration.

[0003] Therefore, it is necessary to provide a project multi-organization collaborative management method, system and device to achieve efficient project management among different organizations. Summary of the invention

[0004] One or more embodiments of the present specification provide a project multi-organization collaborative management method, the method comprising: obtaining unit characteristic factors of multiple participants in a construction project; the unit characteristic factors include the organizational scale, organizational nature, organizational type, organizational region and organizational level of each participant; based on the unit characteristic factors, determining the collaborative structure scale of the construction project; based on the unit characteristic factors and the collaborative structure scale, determining the project type of the construction project; based on historical project data, determining a collaborative structure model corresponding to the construction project; and based on the collaborative structure scale, the project type and the collaborative structure model, generating a collaborative management structure for the construction project.

[0005] One or more embodiments of the present specification also provide a project multi-organization collaborative management system, the system comprising: an acquisition module, configured to acquire unit characteristic factors of multiple participants in a construction project; the unit characteristic factors include the organizational scale, organizational nature, organizational type, organizational region and organizational level of each of the participants; a first determination module, configured to determine the collaborative structure scale of the construction project based on the unit characteristic factors of the multiple participants; a second determination module, configured to determine the project type of the construction project based on the unit characteristic factors of the multiple participants and the collaborative structure scale; a third determination module, configured to determine the collaborative structure mode corresponding to the construction project based on historical project data; and a fourth determination module, configured to generate a collaborative management structure for the construction project based on the collaborative structure scale, the project type and the collaborative structure mode.

[0006] One or more embodiments of the present specification also provide a project multi-organization collaborative management device, the device comprising at least one storage medium and at least one processor; the at least one storage medium is used to store computer instructions; the at least one processor is used to execute the computer instructions to implement the project multi-organization collaborative management method as described in the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a schematic diagram of an application scenario of a project multi-organization collaborative management system according to some embodiments of this specification; Figure 2 is an exemplary flow chart of a project multi-organization collaborative management method according to some embodiments of this specification; Figure 3 is an exemplary flow chart for determining a collaborative structure model corresponding to a construction project according to some embodiments of this specification; Figure 4 is an exemplary flow chart of generating a collaborative management structure according to some embodiments of this specification; Figure 5 is a partial schematic diagram of a collaborative management structure according to some embodiments of this specification; Figure 6 It is an exemplary module diagram of a project multi-organization collaborative management system shown in some embodiments of this specification. DETAILED DESCRIPTION

[0008] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0009] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0010] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0011] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0012] Figure 1 It is a schematic diagram of an application scenario of a project multi-organization collaborative management system according to some embodiments of this specification.

[0013] In some embodiments, Figure 1 As shown, the application scenario 100 of the project multi-organization collaborative management system includes a construction project 110, a network 120, a terminal 130, a processor 140, and a storage device 150.

[0014] Construction project 110 refers to an engineering project that requires collaborative management by multiple organizations. For example, construction project 110 may include various building construction projects (such as residential, commercial centers, school buildings), landscape projects, transportation hub projects (such as airports, railway stations, bus stations, ports, etc.), road and bridge projects, etc. It should be noted that the embodiments of this specification take construction projects as examples for ease of explanation only and do not constitute a limitation. Construction project 110 may also be other projects that require joint management by multiple organizations. For example, construction project 110 may also be a supply chain organization management project, a scientific research unit organization management project, an education system organization management project, a group company organization management project, etc.

[0015] In some embodiments, the construction project 110 may include various materials and project lists related to the project, such as design drawings, material lists, personnel lists, and unit information (such as information related to the construction unit, construction unit, supervision unit, design unit, and suppliers).

[0016] The network 120 may connect the components in the application scenario 100 of the project multi-organization collaborative management system and / or connect other components outside the application scenario 100. In some embodiments, one or more components of the application scenario 100 of the project multi-organization collaborative management system (e.g., the terminal 130, the processor 140, and the storage device 150, etc.) may be connected and / or communicate with each other through the network 120. For example, the terminal 130 may send unit information of the construction project 110 to the processor 140, etc., through the network 120.

[0017] The terminal 130 can provide functional components related to user interaction and can implement user interaction functions (such as providing or displaying information and data to users). The user may refer to a manager of the construction project 110, for example, the user may be a person in a unit related to the construction project, etc. As just an example, the terminal 130 may be a mobile device, a tablet computer, a laptop computer, a desktop computer, etc., or any combination thereof, of other devices having input and / or output functions.

[0018] The processor 140 is capable of processing information and / or data related to the project multi-organization collaborative management system to perform one or more functions described in this specification. In some embodiments, the processor 140 can obtain unit characteristic factors of multiple participants in the construction project; the unit characteristic factors include the organizational scale, organizational nature, organizational type, organizational region and organizational level of each of the participants; based on the unit characteristic factors, the collaborative structure scale of the construction project is determined; based on the unit characteristic factors and the collaborative structure scale, the project type of the construction project is determined; based on historical project data, the collaborative structure model corresponding to the construction project is determined; based on the collaborative structure scale, the project type and the collaborative structure model, the collaborative management structure of the construction project is generated. Detailed description of related content can be found later (such as Figure 2 , Figure 3 etc.) related description.

[0019] In some embodiments, the processor 140 may include a central processing unit (CPU), a digital signal processor (DSP), a microcontroller unit (MCU), a computer, a user console, etc., or any combination thereof. In some embodiments, the processor 140 may include a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 140 may be local or remote. In some embodiments, the processor 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0020] The storage device 150 is capable of storing data, instructions and / or any other information. In some embodiments, the storage device 150 may store data obtained from the terminal 130, the processor 140, etc., such as unit characteristic factors of multiple participants in a construction project, etc. In some embodiments, the storage device 150 may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 may be executed on a cloud platform. In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more other components (e.g., the terminal 130, the processor 140, etc.) of the application scenario 100 of the project multi-organization collaborative management system. In some embodiments, the storage device 150 may be part of the processor 140.

[0021] It is worth noting that the application scenario 100 of the project multi-organization collaborative management system is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various changes and modifications can be made according to the description of this specification. For example, the application scenario 100 of the project multi-organization collaborative management system may also include a database, an information source, and the like. For another example, the application scenario 100 of the project multi-organization collaborative management system may be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not deviate from the scope of this specification.

[0022] Figure 2 is an exemplary flow chart of a project multi-organization collaborative management method according to some embodiments of this specification. In some embodiments, process 200 may be executed by a processing device (eg, processor 140). Figure 2 As shown, process 200 includes the following steps.

[0023] Step 210, obtaining unit characteristic factors of multiple participants of the construction project. In some embodiments, the unit characteristic factors include the organizational scale, organizational nature, organizational type, organizational region and organizational level of each participant.

[0024] Participants refer to units / agencies / organizations that participate in the construction project. Taking the construction project as a residential construction project as an example, the participants may include the construction unit, construction unit, supervision unit, design unit and supplier. For more information about construction projects, please refer to Figure 1 .

[0025] Unit characteristic factors refer to information / characteristics related to the parties involved in the construction project.

[0026] Organizational scale refers to the size and dimensions of each participant in a construction project in terms of number of personnel, total assets, business scope, etc.

[0027] The nature of the organization refers to the legal form and ownership structure of each party involved in the construction project. For example, the nature of the organization may include state-owned enterprises, private enterprises, foreign-invested enterprises, joint ventures, etc.

[0028] Organization type refers to the type of each party involved in a construction project according to its main business and function. For example, the organization type may include design units, construction units, supervision units, cost consulting units, etc.

[0029] The organizational area refers to the area for which each participant in the construction project is responsible.

[0030] Organizational levels refer to the management levels and decision-making structures within each participant in a construction project. For example, organizational levels may include company headquarters, branches, project departments, etc.

[0031] In some embodiments, the processor 140 may obtain unit characteristic factors of multiple participants based on the construction project. For example, the processor 140 may obtain a list of multiple participants related to the project, and obtain the unit characteristic factors of the multiple participants through manual input or a third-party information source (such as the Internet). In some embodiments, the storage device 150 may also store a large number of unit characteristic factors of different participants. The processor 140 may obtain a list of multiple participants through the construction project, and obtain the unit characteristic factors of multiple participants related to the construction project from the storage device 150 based on the list of participants.

[0032] Step 220, determining the collaborative structure scale of the construction project based on the unit characteristic factors.

[0033] The scale of collaborative structure refers to the size of the overall organizational structure for collaborative work in a construction project.

[0034] In some embodiments, the processor 140 may determine the scale of the collaborative structure of the construction project based on the unit characteristic factors of each participant in the construction project. For example, the processor 140 may determine the scale of the collaborative structure based on the number of top-level units in the construction project, the number of department levels of each participant, and the unit characteristic factors of each participant. Exemplarily, the processor 140 may determine the scale of the collaborative structure based on the method shown in formula (1): Among them, S(T) represents the scale of the collaborative structure of construction project T, U(T) represents the number of top-level units of the construction project, and D i (T) represents the department level of the ith participant, B i (T) represents the unit characteristic factor of the i-th participant, and n represents the total number of participants in construction project T.

[0035] The top-level unit refers to the management department responsible for the overall planning, organization, coordination and control of the construction project. For example, the top-level unit may include the general contractor, subcontractor, design, supervision and other departments.

[0036] The number of department levels refers to the number of management levels of the construction project participants from the top management department (such as the project headquarters) to the grassroots execution units (such as construction teams or specific work teams). For example, if a participant includes senior management, middle management, and project execution, the number of department levels of the participant is 3.

[0037] In some embodiments, relevant data such as the number of top-level units of the construction project, the number of department levels of each participant, etc. can be obtained through user input, or can be pre-stored in the storage device 150. The processor 140 can obtain it from the storage device 150 based on the name of the participant.

[0038] Step 230, determining the project type of the construction project based on the unit characteristic factor and the collaborative structure scale.

[0039] Project type refers to the classification of construction projects.

[0040] In some embodiments, the processor 140 may classify the construction project based on the unit characteristic factor and the collaborative structure scale to determine the project type of the construction project. For example, the processor 140 may determine the project type of the construction project based on the functional type of the construction project, the collaborative structure scale of the construction project, the complexity of the construction project, the investment amount of the construction project, and the field of the construction project. Exemplarily, the processor 140 may determine the project type based on the method shown in formula (2): Type(T)=α1·F(T)+α2·S(T)+α3·C(T)+α4·I(T)+α5·D(T) (2) Among them, Type(T) represents the project type of construction project T, F(T) represents the functional type of construction project T, S(T) represents the scale of the collaborative structure of construction project T, C(T) represents the complexity of construction project T, I(T) represents the investment amount of construction project T, D(T) represents the field of construction project T (such as construction field, water conservancy field, energy field, etc.), α1~α5 represent the weights of each factor when determining the project type, and the larger the weight, the greater the influence of the corresponding factor on the project type.

[0041] The functional type of a construction project refers to the classification of a construction project according to its usage function. For example, the functional type of a construction project may include residential facilities, commercial facilities, transportation facilities, etc.

[0042] The complexity of a construction project refers to the complexity of the project during construction. For example, the complexity of a construction project includes technical complexity (including specifications and requirements in different fields such as civil engineering, architectural design, material science, and environmental engineering) and environmental complexity (such as climatic conditions, geological conditions, etc.).

[0043] In some embodiments, the processor 140 can clean and standardize the data of historical projects, determine the factors that affect the project type, such as the functional type, collaborative structure scale, complexity, investment amount, and field, and set the initial weights corresponding to each factor based on the experience of experts in the field (such as construction experts), and adjust the initial weights through methods such as multiple linear regression analysis to determine the weights of each factor that affects the project type. In some embodiments, the processor 140 can also regularly verify and update the weights of each factor that affects the project type based on the latest construction project data.

[0044] Step 240 , determining a collaborative structure model corresponding to the construction project based on historical project data.

[0045] The collaborative structure model refers to a template for determining a multi-organization collaborative management architecture for a construction project. That is, the processor 140 can use the collaborative structure model as a template and determine a multi-organization collaborative management architecture for the current construction project in combination with the collaborative structure scale and project type.

[0046] In some embodiments, the processor 140 may calculate the pattern similarity between the construction project and the historical project scale of the historical project data based on the collaborative structure scale of the construction project; determine the target historical project data based on the pattern similarity; and determine the collaborative structure pattern based on the target historical project data. For more information on related content, please refer to Figure 3 and its related parts.

[0047] Step 250 , generating a collaborative management structure for the construction project based on the collaborative structure scale, project type and collaborative structure model.

[0048] Collaborative management structure refers to the organizational structure that enables multiple organizations to jointly manage construction projects. Figure 5 FIG. 1 is a partial schematic diagram of a collaborative management structure according to some embodiments of this specification. Figure 5 As shown in the figure, unit organization A and unit organization B can collaboratively manage the construction process of project 2. That is, the collaborative management structure includes the relationship structure between the various participants in the construction project, the relationship between the participants and the construction project, and the internal organizational structure of each participant. The collaborative management structure can clarify the relationship between different participants (and their various levels) and the construction project, which facilitates the efficient management of the construction project.

[0049] In some embodiments, the processor 140 can generate a collaborative relationship structure based on the collaborative structure scale, project type, and collaborative structure model; determine the internal organizational structure of each participant based on the unit characteristic factor; and add the internal organizational structure to the collaborative relationship structure to generate a collaborative management structure. For more information on related content, see Figure 4 and related instructions.

[0050] In some embodiments, when a construction project is created, the processor 140 can generate a collaborative management structure through a deployed smart contract; when the organizational structure of the construction project is adjusted, the processor 140 can update the collaborative management structure through the execution of the smart contract; and upload the generation record and update record of the collaborative management structure to the blockchain.

[0051] A smart contract is a computer protocol that automatically executes contracts based on blockchain technology.

[0052] In some embodiments, when a construction project is created, the processor 140 can generate a collaborative management structure for the construction project through a deployed smart contract according to the methods described in steps 210 to 250 above. In some embodiments, when the smart contract is generated, it can also check whether the creation of each node complies with the project collaboration rules. If not, the node is modified.

[0053] In some embodiments, when the organizational structure of a construction project is adjusted (for example, adding or reducing participants), the smart contract can also update the collaborative management structure.

[0054] In some embodiments, smart contracts can also record the creation and changes of each node and organizational structure in the collaborative management structure on the blockchain to ensure the transparency and non-tamperability of the data.

[0055] The project multi-organization collaborative management method described in some embodiments of the present specification supports multiple units to build collaborative organizations based on actual business needs, can intuitively reflect the relationship between different personnel, units and projects, and facilitate efficient management and traceability of projects and organizations; while maintaining the independence of each unit's organizational data, it can also achieve data interoperability between units and between multi-unit organizations and projects, thereby improving project implementation efficiency; it can also achieve interoperability between enterprise organizations and project organizations, thereby ensuring the efficiency and standardization of multi-unit collaborative management.

[0056] Figure 3 is an exemplary flow chart of determining a collaborative structure model corresponding to a construction project according to some embodiments of this specification. In some embodiments, process 300 may be executed by a processing device (e.g., processor 140). Figure 3 As shown, process 300 includes the following steps.

[0057] Step 310 , based on the collaborative structure scale of the construction project, calculate the pattern similarity between the construction project and the historical project scale of the historical project data.

[0058] The historical project scale refers to the scale of the collaborative structure of historical construction projects.

[0059] In some embodiments, a large amount of historical construction project data corresponding to historical construction projects may be stored in the storage device 150. The historical construction project data includes the historical project scale corresponding to each historical construction project.

[0060] Pattern similarity is an indicator that quantifies the degree to which the scale of the collaborative structure of a construction project is similar to the scale of historical projects in the past. For more information on the scale of collaborative structure, see Figure 2 .

[0061] In some embodiments, the processor 140 may determine the pattern similarity between the scale of the construction project and the scale of the historical project based on the collaborative structure scale of the construction project and the historical project scale of the historical construction project by a method as shown in formula (3): Sim1(T1, T2)=Dist1(S(T1), S(T2)) (3) Among them, Sim1(T1, T2) represents the pattern similarity between construction project T1 and historical construction project T2; S(T1) and S(T2) respectively represent the collaborative structure scale of construction project T1 and the historical project scale of historical construction project T2; Dist1() represents the distance function for calculating the similarity between the collaborative structure scale of construction project T1 and the historical project scale of historical construction project T2, such as tree editing distance, graph editing distance, etc.

[0062] Step 320: Determine target historical project data based on pattern similarity.

[0063] The target historical project data refers to project data corresponding to a historical construction project that is most similar to the collaborative structure model of the construction project.

[0064] In some embodiments, the processor 140 may determine the target historical project data based on the pattern similarity. For example, the processor 140 may determine the historical project data with the greatest pattern similarity to the construction project as the target historical project data.

[0065] In some embodiments, the processor 140 may also calculate the type similarity between the construction project and the historical project type of the historical project data based on the project type of the construction project; and determine the target historical project data by weighting the type similarity and the pattern similarity.

[0066] Type similarity is a metric that quantifies how similar the project type of a construction project is to the historical project types of historical construction projects. For more information about project types, see Figure 2 .

[0067] In some embodiments, the processor 140 may determine the pattern similarity between the construction project and the historical project scale based on the project type and the historical project type of the historical construction project by a method as shown in formula (4): Sim2(T1, T2)=Dist2(Type(T1), Type(T2)) (4) Among them, Sim2(T1, T2) represents the type similarity between the construction project T1 and the historical construction project T2; Type(T1) and Type(T2) respectively represent the project type of the construction project T1 and the historical project type of the historical construction project T2; Dist2() represents the distance function for calculating the similarity between the project type of the construction project T1 and the historical project type of the historical construction project T2, such as hash algorithm, semantic similarity, etc.

[0068] In some embodiments, the processor 140 may also perform weighting based on the type similarity and the pattern similarity, and determine the weighted similarity by a method as shown in formula (5): Sim3(T1, T2)=α·Sim1(T1, T2)+β·Sim2(T1, T2) (5) Among them, Sim3(T1, T2) represents weighted similarity, α and β represent the pattern similarity weight for measuring the importance of pattern similarity and the type similarity weight for measuring the importance of type similarity, respectively.

[0069] In some embodiments, the processor 140 can obtain a sample data set; wherein the sample data set includes multiple sample data pairs, each sample data pair includes a sample collaborative structure scale and a sample project type; calculate the pattern similarity and type similarity of each sample data pair; optimize the pattern similarity weight and type similarity weight of each sample data pair; and determine the pattern similarity weight and type similarity weight based on the optimization result.

[0070] Each sample data pair includes two sample data. The sample collaborative structure scale and the sample project type refer to the collaborative structure scale and project type of the sample data. Among them, one sample data can correspond to one historical project data.

[0071] In some embodiments, the processor 140 may collect a large amount of historical construction project data, pair the historical construction project data as a sample data set, and store the sample data set in the storage device 150 .

[0072] In some embodiments, for each sample data pair in the sample data set, the processor 140 may calculate its corresponding pattern similarity and type similarity based on the method shown in formula (3) and formula (4).

[0073] In some embodiments, for the pattern similarity and type similarity of each sample data pair, the pattern similarity weight and type similarity weight may be set based on expert experience.

[0074] In some embodiments, the processor 140 can also optimize the pattern similarity weight and type similarity weight of each sample data pair. Exemplary optimization methods include: using regression analysis least squares method to fit a weight optimization model, the weight optimization model uses pattern similarity and type similarity as independent variables and weighted similarity as dependent variable. In this process, α and β are optimized as regression coefficients to minimize the difference between the weighted similarity predicted by the weight optimization model and the weighted similarity. In some embodiments, the processor 140 can also use cross-validation techniques to evaluate the generalization ability of the weight optimization model and avoid overfitting.

[0075] The optimization result refers to the optimized pattern similarity weight and type similarity weight.

[0076] In some embodiments, the processor 140 may also use a set of independent sample data pairs to determine the weighted similarity of the sample data pairs based on the optimized pattern similarity weights and type similarity weights, and compare the determined weighted similarity with the actual weighted similarity of the sample data pair. When the difference between the two is less than a threshold, the optimized result is used as the pattern similarity weight and the type similarity weight. Otherwise, the pattern similarity weight and the type similarity weight need to be re-optimized based on the sample data set.

[0077] In some embodiments of the present specification, optimizing the pattern similarity weights and type similarity weights based on sample data sets can improve the accuracy of the weights, make the subsequently determined collaborative structure model closer to the construction project, and obtain better project multi-organization collaborative management effects.

[0078] In some embodiments, the processor 140 may also obtain a weight adjustment factor; adjust the weight of the pattern similarity and the weight of the type similarity based on the weight adjustment factor; and determine the weight of the pattern similarity and the weight of the type similarity based on the adjustment result.

[0079] The weight adjustment factor refers to the adjustment ratio and / or adjustment direction of the weight of the pattern similarity and the type similarity weight, wherein the adjustment direction may include increase, decrease, etc.

[0080] In some embodiments, the processor 140 may determine a weight adjustment factor based on a unit characteristic factor of each sample data in the sample data pair.

[0081] In some embodiments, the processor 140 may determine the weight adjustment factor based on a weight determination model. The weight determination model is a machine learning model (such as a neural network model, etc.), whose input includes a unit feature factor of the sample data in each sample data pair, and whose output is a weight adjustment factor.

[0082] In some embodiments, the weight determination model can be obtained by model training through a second training set. The second training set may include a second training sample and a second label, the second training sample includes unit characteristic factors of multiple participants in multiple historical construction projects, and the second label includes weight adjustment factors corresponding to individual unit participants in the construction of multiple historical projects.

[0083] In some embodiments, the second label can be obtained by manual annotation. For example, it can be determined manually based on the project acceptance scores of multiple participants in the historical construction project and the scores of the unit characteristic factors in the historical construction project. The higher the score, the greater the corresponding weight adjustment factor.

[0084] In some embodiments, the processor 140 may determine the target historical project data according to the weighted similarity. For example, the processor 140 may determine the historical project data with the greatest weighted similarity to the construction project as the target historical project data.

[0085] Step 330 , determining a collaborative structure model based on the target historical project data.

[0086] In some embodiments, the processor 140 may determine the collaborative structure pattern of the target historical project data as the collaborative structure pattern of the construction project.

[0087] In some embodiments, the processor 140 may also extract collaborative structural features based on the target historical project data; input the collaborative structural features and unit characteristic factors of multiple participants into a structural pattern generation model to determine a collaborative structural pattern.

[0088] The collaborative structure characteristics refer to the characteristics of the collaborative structure model corresponding to the target historical project data. For example, the collaborative structure characteristics include the number of organizational structure layers, the number of participants, and the relationship between the participants.

[0089] In some embodiments, the collaborative structure feature can be obtained based on a pre-trained feature extraction model. For example, the collaborative structure pattern corresponding to the target historical project data can be input into the pre-trained feature extraction model, and the pre-trained feature extraction model outputs the corresponding collaborative structure feature.

[0090] The structural pattern generation model is a machine learning model that determines the collaborative structural pattern of the construction project. In some embodiments, the structural pattern generation model can be a neural network model, a deep neural network model, etc.

[0091] In some embodiments, the input of the structural pattern generation model includes collaborative structural features and unit characteristic factors of multiple participants, and the output includes a collaborative structural pattern.

[0092] In some embodiments, the structural pattern generation model can be obtained based on the first training set training. The training samples of the first training set include the collaborative structural features of the sample historical project data and the sample unit characteristic factors of the sample participants, and the label is the collaborative structural pattern of the historical project data. In some embodiments, the first training set can be obtained based on the historical construction project data through manual collection and annotation.

[0093] In some embodiments of the present specification, a collaborative structure model that is more suitable for the current construction project can be determined through a machine learning model, so that the subsequently determined collaborative management structure is more accurate, thereby improving the effectiveness and efficiency of multi-organization collaborative management of the project.

[0094] In some embodiments, the processor 140 can also generate a first collaborative structure pattern using a second structure pattern generation model based on unit characteristic factors of multiple participants; determine the second collaborative structure pattern based on target historical project data; extract the common structure and non-common structure of the first collaborative structure pattern and the second collaborative structure pattern; perform equity analysis based on the unit characteristic factors of multiple participants to determine the impact factor of the non-common structure according to the equity analysis results; adjust the non-common structure based on the impact factor; and determine the collaborative structure pattern based on the adjusted non-common structure and common structure.

[0095] The second structural pattern generation model is a machine learning model. In some embodiments, the structural pattern generation model can be a neural network model, a deep neural network model, etc.

[0096] In some embodiments, the input of the second structural pattern generation model includes unit characteristic factors of multiple participants, and the output includes a first collaborative structural pattern, wherein the first collaborative structural pattern refers to a collaborative structural pattern generated based on the second structural pattern generation model.

[0097] In some embodiments, the second structural pattern generation model can be obtained based on the third training set training. The training samples of the third training set include sample unit characteristic factors of sample participants in sample historical project data, and the labels are collaborative structural patterns of historical project data. In some embodiments, the third training set can be obtained based on historical construction project data through manual collection and annotation.

[0098] The second collaborative structure mode refers to a collaborative structure mode directly determined by the target historical project data. For example, the processor 140 may directly determine the collaborative structure mode of the target historical project data as the second collaborative structure mode.

[0099] The shared structure refers to the organizational structure included in both the first collaborative structure model and the second collaborative structure model.

[0100] The non-shared structure refers to an organizational structure that is included in only one of the first collaborative structural mode and the second collaborative structural mode. For example, the non-shared structure may include an organizational structure that is included in the first collaborative structural mode but not included in the second collaborative structural mode, and an organizational structure that is not included in the first collaborative structural mode but included in the second collaborative structural mode.

[0101] In some embodiments, the processor 140 may compare the first collaborative structure pattern with the second collaborative structure pattern to extract the common structure and the non-common structure. For example, the processor 140 may overlap the first collaborative structure pattern with the second collaborative structure pattern, and the completely overlapping part is the common structure, and the non-overlapping part is the non-common structure.

[0102] Equity analysis refers to the evaluation of the importance of multiple parties involved in a construction project to the project. For example, if a party performs the most critical construction tasks, the equity analysis result of the party is that the party has a larger equity ratio.

[0103] In some embodiments, the processor 140 may determine the project contribution of each of the multiple participants in the construction project based on the unit characteristic factors of the multiple participants in the construction project and the project list of the construction project; and determine the equity analysis result based on the project contribution.

[0104] For example, the processor 140 can determine the task participation degree of the participant (e.g., construction project construction proportion, construction period, etc.) based on the unit characteristic factor of the participant and the project list of the construction project. The higher the participation degree of the participant, the greater the project contribution, and the corresponding equity analysis result is a greater equity ratio. The equity ratio of the equity analysis result can be expressed in the form of a numerical value.

[0105] The impact factor of non-shared structures refers to the impact of non-shared structures on construction projects.

[0106] The non-shared structure is the organizational structure of the participants in the construction project. In some embodiments, the greater the equity ratio of the participant's equity analysis result (i.e., the greater the participant's contribution to the construction project), the greater the impact factor of the corresponding non-shared structure on the construction project.

[0107] In some embodiments, the processor 140 may determine the participants corresponding to the non-shared structure, and determine the impact factor of the non-shared structure based on the equity analysis results of the participants corresponding to the non-shared structure. For example, for the four participants A, B, C, and D, the non-shared structure only involves the participants B and C. The processor 140 may determine the equity ratio of the participants B and C (i.e., the project contribution of the participants B and C) from the results of the equity analysis, and determine the impact factor according to the ratio of the equity ratio among all the four participants A, B, C, and D. For example, the ratio is directly determined as the impact factor.

[0108] In some embodiments, the adjustment performed by the processor 140 on the non-shared structure includes increasing the size of the non-shared structure, adjusting the position of the non-shared structure, etc.

[0109] In some embodiments, the processor 140 may adjust the non-shared structure based on the impact factor. For example, the larger the impact factor, the greater the impact of the non-shared structure on the construction project, and the higher the proportion / sophistication of the non-shared structure in the final collaborative structure model.

[0110] In some embodiments, the processor 140 may determine a collaborative structure mode based on the shared structure and the adjusted non-shared structure. For example, the processor 140 may combine the shared structure and the adjusted non-shared structure to determine the collaborative structure mode.

[0111] In some embodiments of the present specification, the collaborative structure model is determined by a shared structure and an adjusted non-shared structure, taking into account the contribution of different participants to the construction project, so that the subsequently determined collaborative management structure is more in line with the needs of the construction project and improves project management efficiency and effectiveness.

[0112] Figure 4 is an exemplary flow chart of generating a collaborative management structure according to some embodiments of this specification. In some embodiments, process 400 may be executed by a processing device (eg, processor 140). Figure 4 As shown, process 400 includes the following steps.

[0113] Step 410 , generating a collaborative relationship structure based on the collaborative structure scale, project type and collaborative structure model.

[0114] The collaborative relationship structure refers to the basic framework for the collaborative management of the construction project by the participating parties. In some embodiments, the collaborative relationship structure can be represented by a tree diagram.

[0115] In some embodiments, the processor 140 may generate a collaborative relationship structure based on the collaborative structure scale, project type, and collaborative structure mode in the manner shown in formula (6): Among them, T root (P) refers to the collaborative relationship structure of construction project P; D refers to the historical project data set; T i It refers to the organizational structure of historical project i (i.e., the collaborative structure model of construction project P); for the meaning of other letters and symbols, please refer to formula (1) to formula (5).

[0116] Step 420, based on the unit characteristic factors, determine the internal organizational structure of each participant.

[0117] In some embodiments, the processor 140 may determine the internal organizational structure of each participant based on the unit characteristic factor in the manner shown in formula (7): Among them, T s (P) refers to the internal organizational structure of the parties involved in the construction project P; S s (P) refers to the internal organizational scale of each participant in the construction project P; S s (T i ) refers to the scale of the collaborative structure of historical construction project i; γ and δ refer to the pattern similarity weight and type similarity weight between the internal organizational structure of the participant and the historical construction project, respectively.

[0118] Step 430, adding the internal organizational structure to the collaborative relationship structure to generate a collaborative management structure.

[0119] In some embodiments, the processor 140 may generate a collaborative management structure based on the method shown in formula (8): T=(T root , {T s1 , T s2 , ..., T sn}) (8) Among them, T represents the collaborative management structure, T s1 , T s2 , ..., T sn They represent participants 1 to n of the construction project respectively. In some embodiments, the processor 140 may add the internal organizational structure of each participant of the construction project into formula (8) to generate a collaborative management structure.

[0120] It should be noted that the above descriptions of processes 200, 300, and 400 are only for illustration and description, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to processes 200, 300, and 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0121] Figure 6is an exemplary module diagram of a project multi-organization collaborative management system according to some embodiments of this specification. Figure 6 As shown, the project multi-organization collaborative management system 600 includes an acquisition module 610 , a first determination module 620 , a second determination module 630 , a third determination module 640 and a fourth determination module 650 .

[0122] The acquisition module 610 is configured to acquire unit characteristic factors of multiple participants of the construction project. In some embodiments, the unit characteristic factors include the organizational scale, organizational nature, organizational type, organizational region and organizational level of each participant.

[0123] The first determination module 620 is configured to determine the collaborative structure scale of the construction project based on unit characteristic factors of multiple participants.

[0124] The second determination module 630 is configured to determine the project type of the construction project based on unit characteristic factors of multiple participants and the scale of the collaborative structure.

[0125] The third determination module 640 is configured to determine a collaborative structure pattern corresponding to the construction project based on the historical project data.

[0126] In some embodiments, the third determination module 640 is further configured to extract collaborative structural features based on the target historical project data; input the collaborative structural features and unit characteristic factors of multiple participants into the structural pattern generation model to determine the collaborative structural pattern.

[0127] In some embodiments, the third determination module 640 is further configured to calculate the type similarity between the construction project and the historical project type of the historical project data based on the project type of the construction project; and determine the target historical project data by weighting the type similarity and the pattern similarity.

[0128] In some embodiments, the third determination module 640 is further configured to obtain a sample data set; the sample data set includes multiple sample data pairs, each sample data pair includes a sample collaborative structure scale and a sample project type; calculate the pattern similarity and type similarity of each sample data pair; optimize the pattern similarity weight and type similarity weight of each sample data pair; based on the optimization results, determine the pattern similarity weight and type similarity weight.

[0129] The fourth determination module 650 is configured to generate a collaborative management structure of the construction project based on the collaborative structure scale, the project type, and the collaborative structure mode.

[0130] In some embodiments, the fourth determination module 650 is further configured to generate a collaborative relationship structure based on the collaborative structure scale, project type and collaborative structure model; determine the internal organizational structure of each participant based on unit characteristic factors; and add the internal organizational structure to the collaborative relationship structure to generate a collaborative management structure.

[0131] In some embodiments, the project multi-organization collaborative management system 600 also includes a management module 660 .

[0132] The management module 660 is configured to generate a collaborative management structure through a deployed smart contract when a construction project is created; to update the collaborative management structure through the execution of the smart contract when the organizational structure of the construction project is adjusted; and to upload the generation record and update record of the collaborative management structure to the blockchain.

[0133] It should be noted that the above description of the project multi-organization collaborative management system 600 and its modules is only for the convenience of description and cannot limit this specification to the scope of the embodiments. Figure 6 The acquisition module 610, the first determination module 620, the second determination module 630, the third determination module 640, the fourth determination module 650 and the management module 660 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0134] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation of this specification.

[0135] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different places in this specification does not necessarily refer to the same embodiment.

[0136] It should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

Claims

1. A project multi-organization collaborative management method, the method comprising: Obtain unit characteristic factors of multiple parties involved in the construction project; The unit characteristic factors include the organizational size, organizational nature, organizational type, organizational region and organizational level of each of the participants; Determining the scale of the collaborative structure of the construction project based on the unit characteristic factors; Determining the project type of the construction project based on the unit characteristic factor and the scale of the collaborative structure; Determining a collaborative structure model corresponding to the construction project based on historical project data; A collaborative management structure of the construction project is generated based on the collaborative structure scale, the project type and the collaborative structure model.

2. The method according to claim 1, wherein generating the collaborative management structure of the construction project based on the collaborative structure scale, the project type and the collaborative structure mode comprises: Generate a collaborative relationship structure based on the collaborative structure scale, the project type and the collaborative structure model; Determining the internal organizational structure of each of the participants based on the unit characteristic factors; The internal organizational structure is added to the collaborative relationship structure to generate the collaborative management structure.

3. The method according to claim 1, wherein determining the collaborative structure mode corresponding to the construction project based on historical project data comprises: Based on the collaborative structure scale of the construction project, calculating the pattern similarity between the construction project and the historical project scale of the historical project data; Based on the pattern similarity, determining target historical project data; The collaborative structure model is determined based on the target historical project data.

4. The method according to claim 3, wherein determining the collaborative structure model based on the target historical project data comprises: Extracting collaborative structural features based on the target historical project data; The collaborative structural features and the unit characteristic factors of the multiple participants are input into a structural pattern generation model to determine the collaborative structural pattern.

5. The method according to claim 3, further comprising: Based on the project type of the construction project, calculating the type similarity between the construction project and the historical project type of the historical project data; The target historical item data is determined by weighting the type similarity and the mode similarity.

6. The method according to claim 5, wherein the weight based on the type similarity and the pattern similarity is determined by: Acquire a sample data set; the sample data set includes a plurality of sample data pairs, each of which includes a sample collaborative structure scale and a sample project type; Calculating the pattern similarity and the type similarity of each of the sample data pairs; Optimizing the pattern similarity weight and the type similarity weight of each sample data pair; Based on the optimization result, the weight of the pattern similarity and the weight of the type similarity are determined.

7. The method according to claim 1, further comprising: When the construction project is created, the collaborative management structure is generated through the deployed smart contract; When the organizational structure of the construction project is adjusted, the collaborative management structure is updated through the execution of the smart contract; as well as The generation record and update record of the collaborative management structure are uploaded to the blockchain.

8. A project multi-organization collaborative management system, the system comprising: An acquisition module is configured to acquire unit characteristic factors of multiple participants in the construction project; The unit characteristic factors include the organizational size, organizational nature, organizational type, organizational region and organizational level of each of the participants; A first determination module is configured to determine the collaborative structure scale of the construction project based on unit characteristic factors of the multiple participants; A second determination module is configured to determine the project type of the construction project based on the unit characteristic factors of the multiple participants and the scale of the collaborative structure; A third determination module is configured to determine a collaborative structure mode corresponding to the construction project based on historical project data; The fourth determination module is configured to generate a collaborative management structure of the construction project based on the collaborative structure scale, the project type and the collaborative structure model.

9. The system according to claim 8, further comprising a management module configured to: When the construction project is created, the collaborative management structure is generated through the deployed smart contract; When the organizational structure of the construction project is adjusted, updating the collaborative management structure through the execution of the smart contract; and The generation record and update record of the collaborative management structure are uploaded to the blockchain.

10. A project multi-organization collaborative management device, the device comprising at least one storage medium and at least one processor; The at least one storage medium is used to store computer instructions; The at least one processor is configured to execute the computer instructions to implement the method according to any one of claims 1 to 7.

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